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Journal articles on the topic 'Discrete Wavelet Transforn (DWT)'

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Published: 5 June 2025

Last updated: 15 July 2025

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Tang, Ling, Ming Ju Chen, and Hong Song. "Hybrid Color Image Compression Using Discrete Cosine Transform and Discrete Wavelet Transform." Applied Mechanics and Materials 198-199 (September 2012): 244–48. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.244.

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In this research we undertake a study of image compression based on the discrete cosine transform(DCT) and discrete wavelet transform(DWT). Then a hybrid color image compression algorithm based on DCT and DWT is proposed. This algorithm is implemented through transform the color image using DWT in the YCbCr space first, and then DCT in the low frequency, adopt huffman coding, RLE and arithmetic coding in the encoded mode. In experiments, the results outperform the only DCT and the only DWT typically higher in peak signal-to-noise ratio and have better visual quality.

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Setyono, Andik, and De Rosal Ignatius Moses Setiadi. "Image watermarking using discrete wavelet-tchebichef transform." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1416. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1416-1423.

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Image watermarking is one of the most popular techniques for authenticating copyright on the digital image. Many research on image watermarking has proved that the joint of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) combinations can improve both imperceptibility and robustness when compared to DCT or DWT only. Discrete Tchebichef Transform (DTT) denotes an alternative transformation that has a similarity property with DCT. DTT has an advantage in reducing memory requirements during computing, so the calculation speed is much faster than DCT. This study tested the performance of DTT and DCT on non-blind image watermarking method, where DTT and DCT are performed after DWT. Based on the experimental results, this research proved that the DTT was combined successfully with DWT and very potential for further investigation because it has a computing performance much better than DCT. While the image watermarking quality, both the imperceptibility and robustness aspects were completely identical with the combination of DCT and DWT transformation.

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Guido, Rodrigo Capobianco, Fernando Pedroso, André Furlan, Rodrigo Colnago Contreras, Luiz Gustavo Caobianco, and Jogi Suda Neto. "CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 06 (2020): 2030001. http://dx.doi.org/10.1142/s0219691320300017.

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Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

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Mousa Al-Khuzaay, Maryam I., and Waleed A. Mahmoud Al-Jawher. "New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification." International Journal of Innovative Computing 13, no. 1-2 (2023): 15–21. http://dx.doi.org/10.11113/ijic.v13n1-2.414.

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The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.

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Apu Hosen, Md, Shahadat Hoshen Moz, Sk Shalauddin Kabir, Md Nasim Adnan, and Syed Md. Galib. "In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 581. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp581-590.

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Digital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions.

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Hosen, Md. Apu, Shahadat Hoshen Moz, Sk. Shalauddin Kabir, Md. Nasim Adnan, and Syed Md. Galib. "In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 581–90. https://doi.org/10.11591/ijeecs.v33.i1.pp581-590.

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Toda, Hiroshi, Zhong Zhang, and Takashi Imamura. "Practical design of perfect-translation-invariant real-valued discrete wavelet transform." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (2014): 1460005. http://dx.doi.org/10.1142/s0219691314600054.

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The real-valued tight wavelet frame having perfect translation invariance (PTI) has already proposed. However, due to the irrational-number distances between wavelets, its calculation amount is very large. In this paper, based on the real-valued tight wavelet frame, a practical design of a real-valued discrete wavelet transform (DWT) having PTI is proposed. In this transform, all the distances between wavelets are multiples of 1/4, and its transform and inverse transform are calculated fast by decomposition and reconstruction algorithms at the sacrifice of a tight wavelet frame. However, the real-valued DWT achieves an approximate tight wavelet frame.

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Maulida, Kurnia. "Studi Komparasi Video Watermarking dengan Algoritma Discrete Wavelet Transform dan Discrete Cosine Transform." MATHunesa: Jurnal Ilmiah Matematika 8, no. 3 (2020): 254–60. http://dx.doi.org/10.26740/mathunesa.v8n3.p254-260.

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Perkembangan teknologi di era digital berkembang dengan sangat cepat setiap harinya. Dengan perkembangan teknologi digital saat ini, media sosial dapat memudahkan kita untuk mengakses serta mendistribusikan teks, gambar, video, dan lainnya. Risiko terjadinya kejahatan di video lebih tinggi dibandingkan dengan teks dan gambar. Digital watermarking dapat digunakan untuk melindungi informasi digital dari manipulasi dan distribusi illegal. Penyisipan watermark umumnya dilakukan dalam domain spasial atau domain frekuensi. Metode yang digunakan adalah Discrete Wavelet Transform (DWT) dan Discrete Cosine Transform (DCT). DWT digunakan untuk mendapatkan komponen multi-resolusi yaitu horisontal, vertikal dan diagonal dari suatu gambar. Sedangkan, DCT memisahkan setiap blok gambar menjadi pita frekuensi rendah, sedang dan tinggi. PSNR adalah rasio antara kekuatan maksimum yang mungkin dari sinyal dan kekuatan noise yang merusak. Berdasarkan penelitian yang dilakukan penulis semakin besar koefisien DCT/DWT maka semakin bagus kualitas gambar hasil ekstrak. Hasil uji coba menunjukkan nilai PSNR video watermark menggunakan DWT lebih besar daripada nilai PSNR video watermark menggunakan DCT. Namun, selisih nilai PSNR antara DWT dan DCT sangat kecil. Nilai PSNR video watermark lebih kecil dari 30 mengindikasikan kedua video memiliki kemiripan yang rendah. Semakin besar nilai PSNR video semakin bagus kualitas video tersebut.

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Dabou, Raoult Teukam, Innocent Kamwa, Jacques Tagoudjeu, and Francis Chuma Mugombozi. "Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment." Energies 14, no. 23 (2021): 7995. http://dx.doi.org/10.3390/en14237995.

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Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization problem. Stable and unstable dictionary learning are designed based on datasets recorded by simulating thousands of contingencies with varying faults, load, and generator switching on the IEEE 68-bus test system. This separate supervised learning of stable and unstable scenarios allows determining root mean square error (RMSE), useful for online stability status assessment of new scenarios. With respect to the RMSE performance metric in signal reconstruction-based stability prediction, the present analysis demonstrates that [DWT], [DHT|DWT] and [DST|DHT|DCT] are better stability descriptors compared to K-SVD, [DHT], [DCT], [DCT|DWT], [DHT|DCT], [ID|DCT|DST], and [DWT|DHT|DCT] on test datasets. However, the K-SVD approach is faster to execute in both off-line training and real-time playback while yielding satisfactory accuracy in transient stability prediction (i.e., 7.5-cycles decision window after fault-clearing).

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Sudrajat, Ari, and Ayu Hendrati Rahayu. "Watermarking using DCT and DWT on Pneumonia images." Journal of Applied Intelligent System 8, no. 3 (2023): 273–86. http://dx.doi.org/10.33633/jais.v8i3.8914.

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Watermarking is a branch of the data hiding technique. Watermarking is a technique used to insert a copyright label on an image, so that the copyright of the image can be protected. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are techniques that can be used to watermark. In this study, the Discrete Cosine Transform and Discrete Wavelet Transform methods will be used to watermark images to 5 different host images. In the tests carried out, watermarking techniques will be compared using DCT, DWT, DCT-DWT combination and DWT-DCT combination. The results obtained in this study were the highest PSNR value obtained at 41.931, the highest SSIM obtained 0.99515, the highest entropy was also obtained at 7.4186, The best UACI value is 0.0071158 and the best NCPR value is obtained at 93.9068% then, for the best CC value is obtained at 0.99953. As well as the NCC value, the value obtained is the same all in each test, namely with a value of 1.

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Balsa, Jose. "Comparison of Image Compressions: Analog Transformations." Proceedings 54, no. 1 (2020): 37. http://dx.doi.org/10.3390/proceedings2020054037.

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A comparison between the four most used transforms, the discrete Fourier transform (DFT), discrete cosine transform (DCT), the Walsh–Hadamard transform (WHT) and the Haar-wavelet transform (DWT), for the transmission of analog images, varying their compression and comparing their quality, is presented. Additionally, performance tests are done for different levels of white Gaussian additive noise.

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BELKASIM, SAEID, XIANYU HONG, and O. BASIR. "CONTENT BASED IMAGE RETRIEVAL USING DISCRETE WAVELET TRANSFORM." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 01 (2004): 19–32. http://dx.doi.org/10.1142/s0218001404003046.

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Image retrieval plays an important role in a broad spectrum of applications. Contentbased retrieval (CBR) is one of the popular choices in many biomedical and industrial applications. Discrete image transforms have been widely studied and suggested for many image retrieval applications. The Discrete Wavelet Transform (DWT) is one of the most popular transforms recently applied to many image processing applications. The Daubechies wavelet can be used to form the basis for extracting features in retrieving images based on the description of a particular object within the scene. This wavelet is widely used for image compression. In this paper we highlight the common features between compression and retrieval. Several examples are used to test the DWT retrieval system. A comparison between DWT and Discrete Cosine Transform (DCT) is also made. The retrieval system using DWT requires preprocessing and normalization of images, which might slow down the retrieval process. The accuracy of the retrieval using DWT has been significantly improved by incorporating efficient K-Neighbor Nearest Distance (KNND) measure in our system.

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Somasekhar, B., Ch Mohana Krishna, and Y. Murty. "Investigations on wavelet and Fourier transform based channel estimation in MIMO-OFDM system." International Journal of Engineering & Technology 7, no. 2.21 (2018): 228. http://dx.doi.org/10.14419/ijet.v7i2.21.12178.

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In this paper channel estimation methods for MIMO-OFDM system are investigated based on Fourier Transform and Wavelet Transform. The channel estimation algorithm based on Discrete Fourier Transform (DFT) cause energy leakage in multipath channel with non-sample-spaced time delays. Discrete Cosine Transform (DCT) based channel estimator can mitigate the drawback of Discrete Fourier Transform based channel estimator, when the non-sample spaced path delays are available in multipath fading channels. Wavelet based systems provide better spectral efficiency because of no cyclic prefix requirement, with narrow side lobes and also exhibit improved BER performance. Simulation results reveal that the DWT based transform outperforms the conventional DFT and DCT based channel estimator in terms of bit error rate and mean square error.

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Puneeth, Kumar D. N., and N. Eshwarappa M. "An Efficient DWT-DAPM Technique for PAPR Reduction in OFDM System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 1691–93. https://doi.org/10.35940/ijeat.C5531.029320.

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In this paper, Discrete Wavelet Transform (DWT) Orthogonal Frequency Division Multiplexing (OFDM) system is compared with Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) OFDM systems. The channel noise is modelled with A white Gaussian Model (AWGN), the fading is the impairment in the channel and modelled by Rayleigh fading which is frequency selective fading channel and flat fading channel. The comparisons of Peak to Average Power Ratio (PAPR) and Bit Error Rate (BER) are made using modulation techniques such as Differential Amplitude and Phase Modulation (DAPM), Quadrature Amplitude Modulation (QAM) and Pulse Amplitude Modulation (PAM). Simulation results shows that PAPR is 4.497 dB for DWT-DAPM combination, 4.684 dB for DWT-QAM combination and 6.211 dB for DWT- PAM combination at 10-3 Complementary Cumulative Distributive Function (CCDF).The performance Analysis with the combination of DFT, DCT with DAPM, QAM and PAM are also compared. The BER is 0.01816, 0.01806 at 20 dB SNR in frequency selective channel, flat fading channel for DWT-DAPM and for DWT- QAM, AWGN channel BER is 0.01765 at 20dB SNR.

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Er., Kiran Bala Varinderjit Kaur. "ADVANCE DIGITAL IMAGE COMPRESSION USING FAST WAVELET TRANSFORMS COMPARATIVE ANALYSIS WITH DWT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 1062–69. https://doi.org/10.5281/zenodo.57988.

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Image compression means reducing the size of graphics file, without compromising on its quality. Data compression is defined as the process of encoding data using a representation that reduces the overall size of data. This reduction is possible when the original dataset contains some type of redundancy. Digital image compression is a field that studies methods for reducing the total number of bits required to represent an image. This can be achieved by eliminating various types of redundancy that exist in the pixel values. The objective of this paper is to evaluate a set of wavelets for image compression. Image compression using wavelet transforms results in an improved compression ratio. Here in this paper we examined and compared Discrete Wavelet Transform Using wavelet families such as Haar,sym4, and Biorthogonal with Fast wavelet transform. In DWT wavelets are discretely sampled. The Discrete Wavelet Transform analyzes the signal at different frequency bands with different resolutions by decomposing the signal into an approximation and detail information. The study compares DWT and Advanced FWT approach in terms of PSNR, Compression Ratios and elapsed time for different Images. Complete analysis is performed at first, second and third level of decomposition using Haar Wavelet, Symlet and Biorthogonal wavelet. The implementation of the proposed algorithm based on Wavelet Transform. The implementation is done under the Image Processing Toolbox in the MATLAB.  

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Wirastuti, Ni Made Ary Esta Dewi, and Ida Bagus Dharma Dhyaksa. "Transformasi Wavelet dengan Teknik Clipping Filtering untuk Mereduksi PAPR pada OFDM." Jurnal Teknik Elektro 12, no. 1 (2020): 1–8. http://dx.doi.org/10.15294/jte.v12i1.24399.

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Orthogonal Frequency Division Multiplexing (OFDM) is chosen as multiplexing techniques and broadly used in today’s radiocommunication environments to overcome spectrum insufficiency. With several superior advantages, however, OFDM is terribly affected by high peak to average power ratio (PAPR) due to offset frequency errors and local oscillator (LO) frequency synchronization errors. The high PAPR can cause nonlinear distortion, which outcomes in intermodulation and spectral leakage. This study aims to model the use of wavelet transform (discrete wavelet transform (DWT)) to replace Fourier transform (discrete Fourier transform (DFT)) that used in conventional OFDM, later in this paper is termed as DFT-OFDM. Clipping filtering techniques then applied to DWT-OFDM. The model was proposed to reduce PAPR in DFT-OFDM. The model was compared to DFT-OFDM using Matlab simulation method. The performance was evaluated using the Complementary Cumulative Distributive Function (CCDF) vs. PAPR. The results show that at PAPR 10-3for DFT-OFDM, it was produced PAPR of 10.6 dB whereas in DWT-OFDM, using Daubechies orde 7 (Daubechies7), Symlet orde 7 (Symlet7), Coiflet orde 2 (Coiflet2), were reached PAPR 4.8 dB, PAPR 3.3 dB, PAPR 3 dB, respectively. It means Coiflet2 providing the best PAPR reduction among other orthogonal wavelets. By applied clipping filtering to wavelet Coiflet2, it was produced PAPR of 2.9 dB for classical clipping and 2.8 dB for deep clipping. It show that wavelet Coiflet2 with deep clipping provided the best PAPR.

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HU, YI-QIANG, BING-FEI WU, and CHORNG-YANN SU. "A DISCRETE WAVELET TRANSFORM CODEC DESIGN." Journal of Circuits, Systems and Computers 13, no. 06 (2004): 1347–78. http://dx.doi.org/10.1142/s021812660400201x.

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This manuscript presents a VLSI architecture and its design rule, called embedded instruction code (EIC), to realize discrete wavelet transform (DWT) codec in a single chip. Since the essential computation of DWT is convolution, we build a set of multiplication instruction, MUL, and the addition instruction, ADD, to complete the work. We segment the computation paths of DWT according to the multiplication and addition, and apply the instruction codes to execute the operators. Besides, we offer a parallel arithmetic logic unit (PALU) organization that is composed of two multipliers and four adders (2M4A) in our design. Thus, the instruction codes programmed by EIC control the PALU to compute efficiently. Additionally, we establish a few necessary registers in PALU, and the number of registers depends on the wavelet filters' length and the decomposition level. Yet, the numbers of multipliers and adders do not increase as we execute the DWT or the inverse DWT (IDWT) in multilevel decomposition. Furthermore, we deduce the similarity between DWT and IDWT, so the functions can be integrated in the same architecture. Besides, we schedule the instructions; thus, the execution of the multilevel processes can be achieved without superfluous PALU in a single chip. Moreover, we solve the boundary problem of DWT by using the symmetric extension. Therefore, the perfect reconstruction (PR) condition for DWT requirement can be accomplished. Through EIC, we can systematically generate a flexible instruction codes while we adopt different filters. Our chip supports up to six levels of decomposition, and versatile image specifications, e.g., VGA, MPEG-1, MPEG-2, and 1024×1024 image sizes. The processing speed is 7.78 Mpixel/s when the operation frequency, for normal case, is 100 MHz.

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Abdullah, Shahrum, S. N. Sahadan, Mohd Zaki Nuawi, and Zulkifli Mohd Nopiah. "Fatigue Data Analysis Using Continuous Wavelet Transform and Discrete Wavelet Transform." Key Engineering Materials 462-463 (January 2011): 461–66. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.461.

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The wavelet transform is well known for its ability in vibration analysis in fault detection. This paper presents the ability of wavelet transform in fatigue data analysis starts from high amplitude events detection and it is then followed by fatigue data extraction based on wavelet coefficients. Since the wavelet transform has two main categories, i.e. the continuous wavelet transforms (CWT) and the discrete wavelet transform (DWT), the comparison study were carried out in order to investigate performance of both wavelet for fatigue data analysis. CWT represents by the Morlet wavelet while DWT with the form of the 4th Order Daubechies wavelet (Db4) was also used for the analysis. An analysis begins with coefficients plot using the time-scale representation that associated to energy coefficients plot for the input value in fatigue data extraction. Ten extraction levels were used and all levels gave the damage difference, (%∆D) less than 10% with respect to original signal. From the study, both wavelet transforms gave almost similar ability in editing fatigue data but the Morlet wavelet provided faster analysis time compared to the Db4 wavelet. In comparison to have the value of different at 5%, the Morlet wavelet achieved at L= 5 while the Db4 wavelet at L=7. Even though it gave slower analysis time, both wavelets can be used in fatigue data editing but at different time consuming.

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Hamidi, Mohamed, Mohamed El Haziti, Hocine Cherifi, and Mohammed El Hassouni. "A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection." Journal of Imaging 7, no. 10 (2021): 218. http://dx.doi.org/10.3390/jimaging7100218.

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In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

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Jana, Sunit. "A Strong Digital Image Watermarking Techniques Using Transform-Domain Methods." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51005.

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As digital content becomes more widely shared, protecting intellectual property through digital watermarking is essential. This paper offers a detailed review of watermarking techniques that use transform-domain methods, including Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD). These methods provide better resistance to common image processing attacks while maintaining high imperceptibility and supporting various application scenarios. By examining key contributions from recent studies, this article shows the development of hybrid watermarking schemes, compares their performance, and points out trends in colour watermarking and optimization-based methods. The findings are helpful for researchers and practitioners working on secure and durable watermarking systems. Key Words: Digital Image Watermarking ,Transform-Domain Techniques ,Robustness ,Discrete Cosine Transform (DCT) ,Discrete Wavelet Transform (DWT) ,Singular Value Decomposition (SVD) , Hybrid Watermarking.

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Mohammad, Shukri Salman, Eleyan Alaa, and Al-Sheikh Bahaa. "Discrete wavelet transform-based RI adaptive algorithm for system identification." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2383–91. https://doi.org/10.11591/ijece.v10i3.pp2383-2391.

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In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCT-LMS), discrete wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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Toda, Hiroshi, Zhong Zhang, and Takashi Imamura. "Perfect-translation-invariant variable-density complex discrete wavelet transform." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (2014): 1460001. http://dx.doi.org/10.1142/s0219691314600017.

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The theorems giving the conditions for discrete wavelet transforms (DWTs) to achieve perfect translation invariance (PTI) have already been proven, and based on these theorems, the dual-tree complex DWT and the complex wavelet packet transform, achieving PTI, have already been proposed. However, there is not so much flexibility in their wavelet density. In the frequency domain, the wavelet density is fixed by octave filter banks, and in the time domain, each wavelet is arrayed on a fixed coordinate, and the wavelet packet density in the frequency domain can be only designed by dividing an octave frequency band equally in linear scale, and its density in the time domain is constrained by the division number of an octave frequency band. In this paper, a novel complex DWT is proposed to create variable wavelet density in the frequency and time domains, that is, an octave frequency band can be divided into N filter banks in logarithmic scale, where N is an integer larger than or equal to 3, and in the time domain, a distance between wavelets can be varied in each level, and its transform achieves PTI.

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Rajeswari, R., and S. Balamurugan. "Image Super Resolution Enhancement Based on Interpolation of Discrete and Stationary Wavelet Domain." Asian Journal of Computer Science and Technology 1, no. 1 (2012): 60–64. http://dx.doi.org/10.51983/ajcst-2012.1.1.1668.

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In this paper, we propose an image super resolution enhancement technique based on interpolation of the high frequency sub band images obtained by discrete wavelet transform (DWT) using different types of wavelets such as Daubechies 1. Daubechies 2 .., Daubechies 9 haar, and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). We compare the results of different types of wavelets. DWT is applied in order to decompose an input image into different sub bands. Then the high frequency sub bands as well as the input image are interpolated. The estimated high frequency sub bands are being modified by using high frequency sub bands obtained through SWT then all these sub bands are combined to generate a new super-resolved image by using inverse DWT.

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Nahar, A. K. "A Compression Original Image Based On The DDWT Technique And Enhancement SNR." International Journal of Engineering Technology and Sciences 5, no. 3 (2018): 73–89. http://dx.doi.org/10.15282/ijets.v5i3.1132.

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Generally, Discrete wavelet transform (DWT) are good perform a when little to no simple mathematical operations in the wavelet basis, in many applications, wavelet transforms can be severely truncated compressed and retain useful information Image compression. Though, DWT and the divided wavelet transform, still suffering from Poor directionality Lack of phase information, and Shift- sensitivity, which is a major drawback in most the communications systems. The Double-Density Discrete Wavelet Transform (DDDWT) achieves great results compared to previous conventional methods less complexity. Credited with this good result, so due to a simplified account that deal with two-dimensional and three-dimensional images by the way and transformation matrices as if through a matrix multiplication between the picture and the conversion of number DDWT. Moreover, the form of repeated goal is achieved with the optimization process for the appropriate application.

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Prof., A. P. Padol. "IDENTIFICATION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES BY AN EFFECTIVE WAVELET BASED NEURAL CLASSIFIER." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [NC-Rase 18] (November 16, 2018): 52–60. https://doi.org/10.5281/zenodo.1489825.

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This paper presents a wavelet based wavelet based neural for identification and classification of Power Quality disturbances. The disturbances to be classified from the power system under study and it is simulated in Power System Computer Aided Design (PSCAD).In this paper, the Power Quality disturbances to be identified and classified with the help of Discrete Wavelet Transform and Artificial Neural Network. Power Quality disturbances will be detected by using Discrete Wavelet Transform (DWT) and classified by using Artificial Neural Network (ANN). Discrete Wavelet Transform is used to extract the disturbance features in the power signal. Power quality disturbances are localized by Discrete Wavelet Transform in in time and frequency domain. Multi-Resolution Analysis (MRA) technique of Discrete Wavelet Transform is used to decompose the power signal at 7-level which gives detailed and approximate coefficients. These detailed coefficients of DWT are used to calculate Statistical parameters given as input to ANN to classify Power quality disturbances.

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Sonu, Sheela Paul *. Priya.M. "BER PERFORMANCE ANALYSIS OF FFT AND DWT OFDM USING DPSK IN THE PRESENCE OF CFO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 504–9. https://doi.org/10.5281/zenodo.51502.

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Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation scheme which  has been adopted in many wireless communication standards. Conventional OFDM uses Inverse Fourier Transform (IFFT) in the transmitter side and Fast Fourier Transform (FFT) in the receiver side. One of the drawback of FFT OFDM is the performance degradation in the presence of Carrier Frequency Offset (CFO). Many research works proven that replacing the Fast Fourier Transform (FFT) by Discrete Wavelet Transform (DWT) shows better performance. IFFT is replaced by Inverse Discrete Wavelet Transform (IDWT) and FFT is replaced by DWT (Discrete Wavelet Transform) in the conventional OFDM.DPSK modulation scheme is used in this work. In this paper both FFT OFDM and DWT OFDM are compared in the presence of Carrier Frequency Offset (CFO) in Additive White Gaussian Noise (AWGN) channel. Bit Error Rate (BER)of both FFT OFDM and DWT OFDM is compared in the presence of CFO. The result shows that DWT OFDM out performs FFT OFDM.  

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PENG, LIZHONG, and WEITAO YUAN. "HIGHER-DENSITY DUAL TREE DISCRETE WAVELET TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 05 (2007): 815–41. http://dx.doi.org/10.1142/s0219691307002063.

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This paper introduces the higher-density dual-tree (HDDT) discrete wavelet transform (DWT). A new MRA is introduced to describe higher-density DWT and used to obtain the sufficient condition for the HDDT Hilbert transform pair. In designing HDDT filters we use the extended common-factor method which not only includes the common-factor method but also provides exact linear phase bi-frame filters. Both HDDT tight frame and bi-frame (anti)symmetric filter design methods are given. At last, the results of denoising experiments by our newly designed HDDT filters in this paper prove the effectiveness of higher-density dual-tree DWT in the application of image processing.

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Gideon Adventus Simanungkalit, Daniel Syahputra Tarigan, and Dinda Roulita Simangunsong. "Discrete Wavelet Transform (DWT) Based Steganography Implementation." Jurnal Teknik Indonesia 2, no. 01 (2023): 13–17. https://doi.org/10.58471/ju-ti.v2i01.660.

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Steganography is the art of hiding information in a medium in such a way that its presence is undetectable by a third party. One of the techniques used in image steganography is the Discrete Wavelet Transform (DWT), which allows the decomposition of an image into different frequency sub-bands, thus facilitating data embedding without sacrificing visual quality. This paper discusses the manual calculation of the application of DWT in image steganography, including the image decomposition steps, the process of message embedding in a particular sub-band, and image reconstruction using the inverse DWT. Experimental results show that this method is effective in hiding information while maintaining good image quality.

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Lang, Fangnian, Jiliu Zhou, Yuan Yan Tang, Hongnian Yu, Shuang Cang, and Zhaowei Shang. "Characteristics analysis of wavelet coefficients and its applications in image compression." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (2014): 1450028. http://dx.doi.org/10.1142/s0219691314500283.

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Currently, the wavelet transform is widely used in the signal processing domain, especially in the image compression because of its excellent de-correlation property and the redundancy property included in the wavelet coefficients. This paper investigates the redundancy relationships between any two or three components of the wavelet coefficients, the wavelet bases and the original signal. We discuss those contents for every condition according to the continuous form and the discrete form, respectively, by which we also derive a uniform formula which illuminates the inherent connection among the redundancy of the wavelet coefficients, the wavelet bases and the original signals. Finally, we present the application of the wavelet coefficient redundancy property in the still image compression domain and compare the properties of the Discrete Wavelet Transform (DWT) with that of the Discrete Cosine Transform (DCT).

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Jayasree, T., Harison D. Sam, and T. Jayanthy. "Discrete Wavelet Transform for the Detection of Transient Disturbances." Advancement of Signal Processing and its Applications 7, no. 1 (2024): 9–13. https://doi.org/10.5281/zenodo.10673399.

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This paper presents the applications of Discrete Wavelet Transform (DWT) for the detection of transient disturbances. Electrical transients can happen in power systems from different sources and have adverse things on the equipment and dependability of the power system. This paper proposes Discrete Wavelet Transform (DWT) based methodology for the detection of transient disturbances. In this method wavelet coefficients are used for the for finding the amplitude variations of the transient signals at different time and frequencies.

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Sumarno, Linggo. "Evaluating The Performance of DWT-DCT Feature Extraction in Guitar Chord Recognition." International Journal of Applied Sciences and Smart Technologies 6, no. 2 (2024): 417–28. https://doi.org/10.24071/ijasst.v6i2.9972.

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This study presents advancements in audio signal processing techniques, specifically in enhancing the efficiency of guitar chord recognition. It is a continuation of the previous studies, which also aim at minimizing the feature extraction length with the intended performance. This study adopted two signal processing techniques that are common: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for use in the feature extraction method. By conducting a systematic evaluation of two key parameters: frame blocking length and wavelet filter selection, a significant achievement could be achieved. The recognition system managed to obtain chord recognition with an accuracy of up to 91.43%, by using a feature extraction length of only three, which brought about smaller representation than the previous studies. The outcome of this study will help improve the data processing, which can be applied in real time, in this case in Field Programmable Gate Array (FPGA)-based chord recognition systems. Keywords: chord recognition, Discrete Wavelet Transform, Discrete Cosine Transform, feature extraction

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Nigam, Vaibhav, Smriti Bhatnagar, and Sajal Luthra. "Image Denoising Using Wavelet Transform and Wavelet Transform with Enhanced Diversity." Advanced Materials Research 403-408 (November 2011): 866–70. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.866.

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This paper is a comparative study of image denoising using previously known wavelet transform and new type of wavelet transform, namely, Diversity enhanced discrete wavelet transform. The Discrete Wavelet Transform (DWT) has two parameters: the mother wavelet and the number of iterations. For every noisy image, there is a best pair of parameters for which we get maximum output Peak Signal to Noise Ratio, PSNR. As the denoising algorithms are sensitive to the parameters of the wavelet transform used, in this paper comparison of DEDWT to DWT has been presented. The diversity is enhanced by computing wavelet transforms with different parameters. After the filtering of each detail coefficient, the corresponding wavelet transforms are inverted and the estimated image, having a higher PSNR, is extracted. To benchmark against the best possible denoising method three thresholding techniques have been compared. In this paper we have presented a more practical, implementation oriented work.

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Hashim Latif, Israa, Sarah Haider Abdulredha, and Sana Khalid Abdul Hassan. "Discrete Wavelet Transform-Based Image Processing: A Review." Al-Nahrain Journal of Science 27, no. 3 (2024): 109–25. https://doi.org/10.22401/anjs.27.3.13.

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The field of image processing has seen remarkable advancements over the past few decades, and Discrete Wavelet Transform (DWT) has emerged as a powerful tool with in this domain. This review article provides a comprehensive overview of previously published works that focus on DWT’s application in image processing. DWT offers multi-resolution analysis capabilities, making it particularly useful for various image processing tasks such as de-noising, compression, enhancement, and feature extraction. This review explores the fundamental principles of DWT and its mathematical foundations. We delve into its advantages over traditional Fourier Transform methods, particularly its ability to handle non-stationary signals and provide both frequency and spatial information simultaneously. The article surveys the application of DWT in different image processing techniques. It discusses how DWT contributes to the efficiency and effectiveness of image compression algorithms, such as the JPEG 2000 standard, and its role in reducing noise while preserving important image features.

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Alex Rajju Balan, J. A., and S. Edward Rajan. "A novel embedding technique for lossless data hiding in medical images employing histogram shifting method." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (2014): 1450026. http://dx.doi.org/10.1142/s021969131450026x.

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In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

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Amhar, Fahmi, Endang Purnama Giri, Florence Elfriede Sinthauli Silalahi, et al. "Ownership Protection on Digital Elevation Model (DEM) Using Transform-Based Watermarking." ISPRS International Journal of Geo-Information 11, no. 3 (2022): 200. http://dx.doi.org/10.3390/ijgi11030200.

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This research aims to protect Digital Elevation Model (DEM) data from piracy or counterfeiting. An invisible watermark inserted into the data, which will not considerably change the data value, is necessary. The proposed method involves the use of the two-dimensional discrete cosine transform (2D DCT), a combination of 2D DCT and discrete wavelet transform (DWT), and two-dimensional discrete Fourier transform (2D DFT) in the frequency domain. The data used include a National DEM file downloaded from the geoportal of the Geospatial Information Agency (Badan Informasi Geospasial—BIG). Three files represent mountainous, lowland/urban, and coastal areas. An “attack” is also conducted on the watermarked DEM by cropping. The results indicate that the watermarked DEM is well recognized. The watermark can be read 100% for 2D DCT, while that for 2D DFT can be read 90.50%. The distortion value of the elevation data under the DCT technique demonstrates the smallest maximum value of 0.1 m compared with 4.5 and 1.1 m for 2D DFT and 2D DCT–DWT. Meanwhile, the height difference (Max Delta), the peak signal-to-noise ratio, and the root mean squared error (RMSE) are highest in mountainous, lowland, and coastal areas, respectively. Overall, the 2D DCT is also superior to the 2D DFT and the2D DCT–DWT. Although only one can recognize the nine watermarks inserted on each sheet, DEMs attacked by the cropping process can still be identified. However, this finding can sufficiently confirm that DEMs belong to BIG.

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Aqil, Mounaim, Atman Jbari, and Abdennasser Bourouhou. "ECG Signal Denoising by Discrete Wavelet Transform." International Journal of Online Engineering (iJOE) 13, no. 09 (2017): 51. http://dx.doi.org/10.3991/ijoe.v13i09.7159.

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The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.

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Zhang, Zhong, Jin Ohtaki, Hiroshi Toda, Takashi Imamura, and Tetsuo Miyake. "A novel variable filter band discrete wavelet transform: Application." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (2014): 1460007. http://dx.doi.org/10.1142/s0219691314600078.

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In this study, in order to verify the effectiveness of the variable filter band discrete wavelet transform (VFB-DWT) and construction method of the variable-band filter (VBF), a fetal ECG extraction has been carried out and the main results obtained are as follows. The approach to configuration VBF by selecting the frequency band only where the fetal ECG component is present was effective to configure the optimal base sensible signal. The extraction of the fetal ECG was successful by applying the wavelet shrinkage to VFB-DWT, which used the constructed VBF. The information entropy was selected as an evaluation index, and two kinds of ECG signals are used to evaluate the wavelet transform basis between the wavelet packet transform (WPT) and the VFB-DWT. One is a synthesized signal composed of white noise, the maternal ECG and the fetal ECG. The other signal is the real target signal separated by independent component analysis (ICA) and has the mother's body noise, the maternal ECG and the fetal ECG. The result shows that the basis by VBF of the VFB-DWT is better than the basis of the WPT that was chosen by the best basis algorithm (BBA).

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Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification." Remote Sensing 11, no. 24 (2019): 2906. http://dx.doi.org/10.3390/rs11242906.

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In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.

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Kamal, Nor Ashikin Mohamad, Azuraliza Abu Bakar, and Suhaila Zainudin. "GPCR Protein Feature Representation using Discrete Wavelet Transform and Particle Swarm Optimisation Algorithm." International journal of Multimedia & Its Applications 14, no. 5 (2022): 1–16. http://dx.doi.org/10.5121/ijma.2022.14501.

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Features play an important role in representing classes in the hierarchy structure, and using unsuitable features will affect classification performance. The discrete wavelet transform (DWT) approach provides the ability to create the appropriate features to represent data. DWT can produce global and local features using different wavelet families and decomposition levels. These two parameters are essential to obtain a suitable representation for classes in the hierarchy structure. This study proposes using a particle swarm optimisation (PSO) algorithm to select the suitable wavelet family and decomposition level for G-protein coupled receptor (GPCR) hierarchical class representation. The results indicate that the PSO algorithm mostly selects Biorthogonal wavelets and decomposition level 2 to represent GPCR protein. Concerning the performance, the proposed method achieved an accuracy of 97.9%, 85.9%, and 77.5% at the family, subfamily, and sub-subfamily levels, respectively.

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Vanchak, Vitalii, and Stepan Melnychuk. "Discrete wavelet transform denoising method efficiency evaluation for processing pulse signals with harmonic components." Scientific journal of the Ternopil national technical university 116, no. 4 (2024): 124–34. https://doi.org/10.33108/visnyk_tntu2024.04.124.

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This article reviews the problem of parameter selection for denoising methods based on the Discrete Wavelet Transform (DWT) for processing geo-signals with various noise types and external interference, followed by evaluating the effectiveness in detecting recurring signal patterns. The study reviews the theoretical impact of denoising parameters, existing wavelet and decomposition level selection methods, publications on DWT applications in different fields, and the computational challenges of increasing decomposition levels for microcontrollers. Experimental results of DWT denoising application on field-gathered signals recorded in different environments, presented as average SNR changes for specific DWT parameter combinations. Comparison of results by decomposition levels showed gradual improvements in efficiency with certain wavelets and significant drops after specific levels in some cases due to the filtering of typical samples, which emphasizes the need to review DWT parameters only in the scope of specific parameter combinations. Notable anomalies in efficiency due to the non-stationary nature of signals and parameter resonance with noise or patterns were also observed, requiring further research. Based on the findings, the most effective parameter combinations for denoising the studied geo-signal were identified, with a particularly optimal combination of three decomposition levels, hard thresholding, and rbio3.3 wavelet, which preserved and even amplified signal energy while enabling the detection of typical fragments at distances of 120–100 meters.

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Devi, Sunita. "Image Compression Using Discrete Cosine Transform (DCT) & Discrete Wavelet Transform (DWT) Techniques." International Journal for Research in Applied Science and Engineering Technology V, no. X (2017): 1689–96. http://dx.doi.org/10.22214/ijraset.2017.10246.

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Younis, Dr Hameed A. "Robust Image Watermarking in the Wavelet Domain." Journal of Kufa for Mathematics and Computer 1, no. 2 (2010): 23–34. http://dx.doi.org/10.31642/jokmc/2018/010203.

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The growth of new imaging technologies has created a need for techniques that can be used for copyright protection of digital images. In this paper, a new and robust spread spectrum basedwatermarking scheme has been proposed.The proposed scheme depend on both Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). First, we decompose the image by DWT into asingle level. Then, the approximation part is divided into blocks. The embedding is done in an adaptive fashion depending on the mean (M) of the block. A chaotic sequence of real numbers, depends on asecret key, is embedded as a watermark in the DCT coefficients of the selected blocks. Detection stage generates a watermark which would be compared with the original watermark, by the correlation measure, to determine the existing of the watermark or not. Different tests have been experimented to explain the transparency and the robust of the proposed scheme.

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ZHANG, YUDONG, SHUIHUA WANG, YUANKAI HUO, LENAN WU, and AIJUN LIU. "FEATURE EXTRACTION OF BRAIN MRI BY STATIONARY WAVELET TRANSFORM AND ITS APPLICATIONS." Journal of Biological Systems 18, spec01 (2010): 115–32. http://dx.doi.org/10.1142/s0218339010003652.

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Wavelet transform is widely used in feature extraction of magnetic resonance imaging. However, the traditional discrete wavelet transform (DWT) suffers from translation variant property, which may extract significantly different features from two images of the same subject with only slight movement. In order to solve this problem, this paper utilizes stationary wavelet transform (SWT) to extract features instead of DWT. Experiments on a normal brain MRI demonstrate that wavelet coefficients via SWT are superior to those via DWT, in terms of translation invariant property. In addition, we applied SWT to normal and abnormal brain classification. The results demonstrate that SWT-based classifier is more accurate than that of DWT.

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Vimala, C., and P. Aruna Priya. "Image quality improvement using dddtdwt." International Journal of Engineering & Technology 7, no. 3.3 (2018): 416. http://dx.doi.org/10.14419/ijet.v7i2.33.14197.

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The enhancement of degraded images using different wavelet transform techniques are presented in this paper. The performance of the wavelet techniques is analysed in terms of Peak Signal to Noise Ratio values and Root Means Square error. The Double Density Dual Tree Discrete Wavelet Transform technique is mainly focused for analysis and the results are compared with discrete wavelet transform and the Double Density DWT.

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Chen, Yu Min, Fei Zeng, Jing Yang Wu, Qiao Wan, and Zhi Jun Su. "GPU-Accelerated Discrete Wavelet Transform for Images." Advanced Materials Research 718-720 (July 2013): 2086–91. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2086.

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Discrete Wavelet Transform (DWT) has been brought into wide use in image processing, but it cant fit the demand of the hugeimage data because the time of computing is vast. The GPU is an attractive platform for a broad fieldof applications,which remains asignificanthigharithmetic processingcapability. Therefore itcan beusedasa powerful accelerator without extra cost.CUDA(computeunifieddevicearchitecture) providesahardwareandsoftwareenvironment touse the GPU to accelerate the DWT for images. In this paper, we use the NVIDIA GeForce GT 650M that complies with the CUDA to improvethe execution time of theDiscrete Wavelet Transformfor images. TheresultofexperimentsindicatesthattheCUDAtechnology hastheadvantagesof parallel processingandtheefficiencyofimagetransform isimprovedgreatly. Whats more, it performs better on the larger size image (the max speedup is 15.9).

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Zhang, Zhong, Hiroshi Toda, Takashi Imamura, and Tetsuo Miyake. "A novel variable filter band discrete wavelet transform: Theory and principle." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 04 (2014): 1460006. http://dx.doi.org/10.1142/s0219691314600066.

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It is well-known that a mother wavelet for the discrete wavelet transform (DWT) has the band-pass filter characteristic with octave width in the frequency domain and can be used for octave analysis. However, it is possible that the octave analysis is not necessarily the most suitable to match the analysis signal. In this study, in order to construct the most suitable basis to match the analysis signal, a novel variable-filter band discrete wavelet transform (VFB-DWT) is proposed. It is achieved by using variable-band filters instead of conventional decomposition and reconstruction sequences, which are designed in consideration of the real signal characteristics. Additionally, it is proven that perfect reconstruction of the analysis signal by VFB-DWT is guaranteed using the perfect shift invariant theorem that underlies the theory of the PTI-CDWT having base DWT.

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AMRAOUI, Abdelkader, and Slami SAADI. "Comparison of Energy Rate of a Level 3 Speech Signal using DWT with the Mother Wavelet Haar , meyer , coiflets , symlets , daubechies , biorthogonal." International Conference on Pioneer and Innovative Studies 1 (June 13, 2023): 446–50. http://dx.doi.org/10.59287/icpis.871.

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The objective of this work is to compare the energy rate of a level 3 speech signal using thediscrete wavelet transform (DWT) of various mother wavelets such as Haar, Meyer, biorthogonal, Coiflets,Symlets, and Daubechies, to determine the range of values for both genders of the speaker. The speechsignal is filtered by an 8th order Butterworth filter.We perform the discrete wavelet transform (DWT) on the mother wavelet to obtain the energy values ofeach mother wavelet at the third level of the decomposition for male and female speakers. We then comparethe energy values of speech signals between men and women speaking the same sentence, This comparisonis conducted on several databases containing repeated sentences for the same individual or both genders,the results are found to be acceptable for further analysis.

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Oudah, Manal K., Rula S. Khudhair, Saad M. Kaleefah, and Aqeela N. Abed. "Improvement of Image Steganography Using Discrete Wavelet Transform." Engineering and Technology Journal 38, no. 1A (2020): 83–87. http://dx.doi.org/10.30684/etj.v38i1a.266.

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Recently the Discrete-Wavelet-Transform (DWT) has been represented as signal processing powerful tool to separate the signal into its band frequency components. In this paper, improvement of the steganography techniques by hiding the required message into the suitable frequency band is presented. The results show that the increase of the message length will reduce the Peak Signal to Noise Ratio (PSNR), while the PSNR increases with the increasing the DWT levels. It should be noted that the PSNR reduction was from -13.8278 to -17.77208 when increasing the message length from 161 to 505 characters. In this context, the PSNR is increased from -13.8278 to 7.0554 and from -17.7208 to 1.7901 when the DWT increased from level (1) to level (2).

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Salman, Mohammad Shukri, Alaa Eleyan, and Bahaa Al-Sheikh. "Discrete wavelet transform-based RI adaptive algorithm for system identification." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2383. http://dx.doi.org/10.11591/ijece.v10i3.pp2383-2391.

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In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

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Khani, Mahmoud E., and Mohammad Hassan Arbab. "Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy." Sensors 22, no. 6 (2022): 2305. http://dx.doi.org/10.3390/s22062305.

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Abstract:

Wavelet transform is an important tool in the computational signal processing of terahertz time-domain spectroscopy (THz-TDS) measurements. Despite its prevalence, the effects of using different forms of wavelet transforms in THz-TDS studies have not been investigated. In this paper, we explore the implications of using the maximal overlap discrete wavelet transform (MODWT) versus the well-known discrete wavelet transform (DWT). We demonstrate that the spectroscopic features extracted using DWT can vary over different overlapping frequency ranges. On the contrary, MODWT is translation-invariant and results in identical features, regardless of the spectral range used for its implementation.We also demonstrate that the details coefficients obtained by the multiresolution analysis (MRA) using MODWT are associated with zero-phase filters. In contrast, DWT details coefficients suffer from misalignments originated from the down- and upsampling operations in DWT pyramid algorithm. Such misalignments have adverse effects when it is critical to retain the exact location of the absorption lines. We study the differences of DWT and MODWT both analytically and experimentally, using reflection THz-TDS measurements of α-lactose monohydrate. This manuscript can guide the researchers to select the right wavelet analysis tool for their specific application of the THz spectroscopy.

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