Journal articles: 'Discrete wavelet transform (DWT)'

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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 (August 28, 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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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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Balsa, Jose. "Comparison of Image Compressions: Analog Transformations." Proceedings 54, no. 1 (August 21, 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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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 (November 30, 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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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 (December 1, 2019): 1416. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1416-1423.

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<span>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.</span>

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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 (November 21, 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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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 (February 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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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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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 (July 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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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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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 (April 20, 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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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 (December 27, 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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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 (December 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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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 (July 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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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 (October 19, 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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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 (June 20, 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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Sy, Nguyen Chi, Ha Hoang Kha, and Nguyen Minh Hoang. "An Efficient Digital Watermarking Technique for Color Images Using Directional Transforms." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 2 (December 31, 2017): 1. http://dx.doi.org/10.31130/jst.2017.57.

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This paper is concerned with a digital watermarking technique for color images based on directional transforms. Different from the traditional watermarking schemes which embed the watermarks into the spatial domain or frequency domain of the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), this paper investigates the performance of the watermarking schemes using the Fast Discrete Curvelet Transforms (FDCT) and Contourlet Transform (CT). We evaluate the performance of the watermarking schemes using the directional transforms on a standard database of color images in terms of invisibility and robustness. The performance metrics are measured by Peak Signal-to-Noise Ratio (PSNR), Normalized Correlation (NC), Structural SIMilarity (SSIM) and required time for extracting and embedding process. The experimental results reveal that watermarking schemes in the directional transform domains outperform the other schemes in DWT domains.

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Kekre, Dr H. B., Dr Tanuja Sarode, and Prachi Natu. "STUDY OF INCREASE IN GLOBAL COMPONENTS IN HYBRID WAVELETS ON DATA COMPRESSION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 2 (July 15, 2013): 1028–39. http://dx.doi.org/10.24297/ijct.v9i2.4173.

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This paper presents a hybrid wavelet transform technique which studies the effect of global components on the quality of image compression. Hybrid wavelet transform is generated using two different component orthogonal transforms. One orthogonal transform represents global featuresof image in betterway and another is used to represent local features. Walsh transform of size 8x8 is used as a base transform i.e. to represent global characteristics of image. Other transforms like DCT, Discrete Real Fourier Transform,DiscreteHartley transform (DHT), Discrete Sine Transform (DST), Discrete Kekre Transform (DKT) and Slant transform of size 32x32 are used to focus on local characteristics of an image.256x256 hybrid wavelet transform is generated and multiple iterations of global components are included using columns of base transform and its effect on reconstructed image quality is observed in terms of Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). Â From the experiments it has been observed that when DCT is used to extract local features, best results are obtained among all combinations with Walsh transform. These results are also compared with Walsh transform and observed to be much superior at higher compression ratios giving better image quality.

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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 (September 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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Akilandeswari, A., Annie Grace Vimala, and D. Sungeetha. "A Low Power Shift Add Multiplier for Lifting Based Dwt using Kogge Stone Adder." International Journal of Engineering and Advanced Technology 9, no. 4 (April 30, 2020): 1080–86. http://dx.doi.org/10.35940/ijeat.c6211.029320.

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The most common technique used for image processing applications is ‘The wavelet transformation’. The Discrete Wavelet Transform (DWT) keeps the time as well as frequency information depend on a multi resolution analysis structure, where the other classical transforms like Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) will not do that. Because of this feature, the quality of the repaired image is improved when comparing to the other transforms. To implement the DWT on a real time codec, a fast device needs to be targeted. While comparing with the other implementation such as PCs, ARM processors, DSPs etc, Field Programmable Gate Array (FPGA) implementation of DWT had better processing speed and costs were vey less. A Fast Architecture based DWT using Kogge Stone Adder is proposed in this paper where the coefficients of lifting scheme are calculated by using Shift adder and Kogge Stone Adder where other techniques used multiplier. The most important intention of the suggested technique is to use minimum calculation and limited memory. The simulation of the suggested design is dole out on the Xilinx 14.1 style tool and also the performance is evaluated and compared with the present architectures.

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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 (March 16, 2022): 2305. http://dx.doi.org/10.3390/s22062305.

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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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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 (October 30, 2017): 1689–96. http://dx.doi.org/10.22214/ijraset.2017.10246.

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Suma, M. N., S. V. Narasimhan, and B. Kanmani. "Interspersed discrete harmonic wavelet packet transform based OFDM — IHWT OFDM." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 03 (May 2014): 1450034. http://dx.doi.org/10.1142/s0219691314500349.

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A new interspersed orthogonal frequency division multiplexing (OFDM) based on Hadamard coded Discrete Harmonic wavelet transform (DHWT), is proposed for complex modulation under exponential channel model. In this method, real part of complex signal is transformed by DCHWT and imaginary part by DSHWT and summed to form interspersed harmonic wavelet based OFDM (IHWT OFDM). DHWT exploits the useful properties of DCT and DST viz., energy compaction/low leakage, frequency resolution and its real nature, compared to DFT. This wavelet is simple as it has reduced processing due to its harmonic wavelet nature. The harmonic nature has built in decimation, easy interpolation by concatenation of different scales in frequency (DCT and DST) domain without associated anti-aliasing filters for analysis, image rejection filters and complicated delay compensation for synthesis. In this work, we have explored advantages of DHWT to implement IHWP-OFDM for QPSK modulated signals. Hadamard codes are employed in proposed method to further improve BER and PAPR performance. New OFDM provides PAPR reduction of 2.6, 3.8 and 1.4 dB as compared to Haar, Daubechies WT OFDM and DFT, respectively.

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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 (May 5, 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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Aqil, Mounaim, Atman Jbari, and Abdennasser Bourouhou. "ECG Signal Denoising by Discrete Wavelet Transform." International Journal of Online Engineering (iJOE) 13, no. 09 (September 22, 2017): 51. http://dx.doi.org/10.3991/ijoe.v13i09.7159.

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<p>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.</p>

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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 (May 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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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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Journal, Baghdad Science. "Combined DWT and DCT Image Compression Using Sliding RLE Technique." Baghdad Science Journal 8, no. 3 (September 4, 2011): 832–39. http://dx.doi.org/10.21123/bsj.8.3.832-839.

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A number of compression schemes were put forward to achieve high compression factors with high image quality at a low computational time. In this paper, a combined transform coding scheme is proposed which is based on discrete wavelet (DWT) and discrete cosine (DCT) transforms with an added new enhancement method, which is the sliding run length encoding (SRLE) technique, to further improve compression. The advantages of the wavelet and the discrete cosine transforms were utilized to encode the image. This first step involves transforming the color components of the image from RGB to YUV planes to acquire the advantage of the existing spectral correlation and consequently gaining more compression. DWT is then applied to the Y, U and V color space information giving the approximate and the detail coefficients. The detail coefficients are quantized, coded using run length encoding (RLE) and SRLE. The approximate coefficients were coded using DCT, since DCT has superior compression performance when image information has poor power concentration in high frequency areas. This output is also quantized, coded using RLE and SRLE. Test results showed that the proposed DWT DCT SRLE system proved to have encouraging results in terms of Peak Signal-to-Noise Ratio (PSNR), Compression Factor (CF) and execution time when compared with some DWT based image compressions.

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Amhar, Fahmi, Endang Purnama Giri, Florence Elfriede Sinthauli Silalahi, Shelvie Nidya Neyman, Anggrahito, Dadan Ramdani, Danang Jaya, et al. "Ownership Protection on Digital Elevation Model (DEM) Using Transform-Based Watermarking." ISPRS International Journal of Geo-Information 11, no. 3 (March 16, 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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Robertson, A. N., K. C. Park, and K. F. Alvin. "Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data." Journal of Vibration and Acoustics 120, no. 1 (January 1, 1998): 261–66. http://dx.doi.org/10.1115/1.2893815.

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This paper addresses the use of discrete wavelet transforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transform (DWT). They are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach. Numerical examples demonstrate that the present DWT-based structural system identification procedure is a serious alternative to the FFT-based procedure, and outperforms FFT methods for narrow frequency-band inputs.

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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 (February 9, 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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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 (July 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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Vimala, C., and P. Aruna Priya. "Image quality improvement using dddtdwt." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 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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Hayati, Raisah, and Rahmadi Kurnia. "Simulasi Unjuk Kerja Discrete Wavelet Transform (DWT) dan Discrete Cosine Transform (DCT) untuk Pengolahan Sinyal Radar di Daerah yang Ber-Noise Tinggi." Jurnal Nasional Teknik Elektro 3, no. 1 (March 1, 2014): 32–43. http://dx.doi.org/10.20449/jnte.v3i1.53.

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Mahmoud, Walid Amin. "Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform." Journal Port Science Research 4, no. 2 (December 8, 2021): 102–8. http://dx.doi.org/10.36371/port.2020.2.7.

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A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff. The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.

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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 (October 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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Ismail, Jasim Mohammed Salih. "Digital Watermarking in Color Image Based On Joint Between DCT and DWT." Ibn AL-Haitham Journal For Pure and Applied Sciences 30, no. 1 (June 11, 2017): 237–45. http://dx.doi.org/10.30526/30.1.1073.

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The massive distribution and development in the digital images field with friendly software, that leads to produce unauthorized use. Therefore the digital watermarking as image authentication has been developed for those issues. In this paper, we presented a method depending on the embedding stage and extraction stag. Our development is made by combining Discrete Wavelet Transform (DWT) with Discrete Cosine Transform (DCT) depending on the fact that combined the two transforms will reduce the drawbacks that appears during the recovered watermark or the watermarked image quality of each other, that results in effective rounding method, this is achieved by changing the wavelets coefficients of selected DWT sub bands (HL or HH), followed by applying DCT transform on the selected sub band's coefficients, this method focuses on the invisibility for the embedded watermark bits, and the quality for the watermarked image; furthermore it focuses on a subjective for the recovered watermark after extraction stage. The proposed method was evaluated by using simple image quality matrix illustrated in the results, and it was found that the proposed method provide good objective quality, the recovered watermark extracted successfully and the quality of recovered watermark are survived.

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SUN, YANKUI, YONG CHEN, and HAO FENG. "TWO-DIMENSIONAL STATIONARY DYADIC WAVELET TRANSFORM, DECIMATED DYADIC DISCRETE WAVELET TRANSFORM AND THE FACE RECOGNITION APPLICATION." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 03 (May 2011): 397–416. http://dx.doi.org/10.1142/s0219691311004110.

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Currently, two-dimensional dyadic wavelet transform (2D-DWT) is habitually considered as the one presented by Mallat, which is defined by an approximation component, two detail components in horizontal and vertical directions. This paper is to introduce a new type of two-dimensional dyadic wavelet transform and its application so that dyadic wavelet can be studied and used widely furthermore. (1) Two-dimensional stationary dyadic wavelet transform (2D-SDWT) is proposed, it is defined by approximation coefficients, detail coefficients in horizontal, vertical and diagonal directions, which is essentially the extension of two-dimensional stationary wavelet transform for orthogonal/biorthogonal wavelet filters. (2) ε-decimated dyadic discrete wavelet transform (DDWT) is introduced and its relation with 2D-SDWT is given, where ε is a sequence of 0's and 1's. (3) Mallat decomposition algorithm based on dyadic wavelet is introduced as a special case of ε-decimated DDWT, and so a face recognition algorithm based on dyadic wavelet is proposed, and experimental results are given to show its effectiveness.

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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 (May 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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Saputra, Pressa Perdana Surya, and Rifqi Firmansyah. "Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means." SinkrOn 8, no. 2 (April 4, 2023): 781–88. http://dx.doi.org/10.33395/sinkron.v8i2.12207.

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Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.

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Debbal, Sid. "Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)." Biomedical Research and Clinical Reviews 4, no. 3 (July 23, 2021): 01–15. http://dx.doi.org/10.31579/2692-9406/052.

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The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

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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 (June 1, 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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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 (October 31, 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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Younis, Dr Hameed A. "Robust Image Watermarking in the Wavelet Domain." Journal of Kufa for Mathematics and Computer 1, no. 2 (October 30, 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, 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 (July 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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Ridsdill‐Smith, T. A., and M. C. Dentith. "The wavelet transform in aeromagnetic processing." GEOPHYSICS 64, no. 4 (July 1999): 1003–13. http://dx.doi.org/10.1190/1.1444609.

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The discrete wavelet transform (DWT) provides an effective and efficient alternative to traditional Fourier and spatial‐convolution processing techniques in the enhancement of aeromagnetic data. Standard operators such as horizontal and vertical derivatives, integrals of any order, and the Hilbert transform can be diagonalized in the wavelet domain, leading to an efficient algorithm. The DWT preserves the spatial localization of the components of the signal, allowing for intelligent discrimination between noise and signal in a given frequency range. This, for example, allows for more accurate calculation of higher order derivatives from noisy signals than is possible with conventional techniques. Additional accuracy can be gained by using a cycle‐spinning algorithm to minimize local artifacts from the DWT denoising procedure.

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Azani Mustafa, Wan, Haniza Yazid, Wan Khairunizam, Mohd Aminuddin Jamlos, I. Zunaidi, Z. M. Razlan, and A. B. Shahriman. "Image Enhancement Based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT): A Review." IOP Conference Series: Materials Science and Engineering 557 (June 28, 2019): 012027. http://dx.doi.org/10.1088/1757-899x/557/1/012027.

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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 (December 5, 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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Zhao, Di. "Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform." International Journal of Mobile Computing and Multimedia Communications 7, no. 2 (April 2016): 22–35. http://dx.doi.org/10.4018/ijmcmc.2016040102.

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Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

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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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Journal articles on the topic 'Discrete wavelet transform (DWT)'

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