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Статті в журналах з теми "Haar Transform"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Liu, Guibo, Dazu Huang, Dayong Luo, Wang Lei, Ying Guo, and Moonho Lee. "Fast Jacket-Haar Transform with Any Size." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/628642.

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Анотація:

Jacket-Haar transform has been recently generalized from Haar transform and Jacket transform, but, unfortunately, it is not available in a case where the lengthNis not a power of 2. In this paper, we have proposed an arbitrary-length Jacket-Haar transform which can be conveniently constructed from the 2-point generalized Haar transforms with the fast algorithm, and thus it can be constructed with any sizes. Moreover, it can be further extended with elegant structures, which result in the fast algorithms for decomposing. We show that this approach can be practically applied for the electrocardi

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2

Király, Balázs. "Generalized Haar–Fourier transform." Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio computatorica, no. 37 (2012): 239–46. https://doi.org/10.71352/ac.37.239.

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Анотація:

We give a new generalization for Haar functions. The generalization starts from the Walsh-like functions and based on the connection between the original Walsh and Haar systems. We generalize the Haar–Fourier Transform too.

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3

Zeelan Basha, CMAK, K. M. Sricharan, Ch Krishna Dheeraj, and R. Ramya Sri. "A Study on Wavelet Transform Using Image Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 94. http://dx.doi.org/10.14419/ijet.v7i2.32.13535.

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The wavelet transforms have been in use for variety of applications. It is widely being used in signal analysis and image analysis. There have been lot of wavelet transforms for compression, decomposition and reconstruction of images. Out of many transforms Haar wavelet transform is the most computationally feasible wavelet transform to implement. The wave analysis technique has an understandable impact on the removal of noise within the signal. The paper outlines the principles and performance of wavelets in image analysis. Compression performance and decomposition of images into different la

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4

Kaiser, G. "The fast Haar transform." IEEE Potentials 17, no. 2 (1998): 34–37. http://dx.doi.org/10.1109/45.666645.

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5

Burns, Thomas J. "Optical Haar wavelet transform." Optical Engineering 31, no. 9 (1992): 1852. http://dx.doi.org/10.1117/12.59917.

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6

Seidel, Henrique, Morgana Da Rosa, Guilherme Paim, Eduardo Costa, Sergio Almeida, and Sergio Bampi. "Exploring Multi-Level Composition and Efficient MCM Schemes for an Energy-Efficient Wavelet Haar Architecture." Journal of Integrated Circuits and Systems 16, no. 2 (2021): 1–9. http://dx.doi.org/10.29292/jics.v16i2.242.

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This work explores multi-level fixed-point Haar transform compositions combined with MCM (multiple constant multiplication) schemes for an energy-efficient hardware architecture. We investigate a set of six lower-level Haar transforms for composing a Haar-9 architecture. The multiple-level Haar transforms use as a base M=1, M=2, and M=3 resolution levels. The processing module (PM) of the Haar explores efficient MCM schemes. The architectures were described in VHDL and synthesized employing the ST 65nm CMOS cell library. The results show that Haar-II architecture presents the lower circuit are

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7

Camelo, Andres Felipe, Carlos Alberto Ramírez, and José Rodrigo González. "Function Representation in Hilbert Spaces Using Haar Wavelet Series." Statistics, Optimization & Information Computing 13, no. 6 (2025): 2477–86. https://doi.org/10.19139/soic-2310-5070-2288.

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This work explores the application of integral transforms using Scale and Haar wavelet functions to numerically represent a function \( f(t) \). It is based on defining a vector space where any function can be represented as a linear combination of orthogonal basis functions. In this case, the Haar wavelet transform is used, employing Haar functions generated from Scale functions. First, the fundamental mathematical concepts such as Hilbert spaces and orthogonality, necessary for understanding the Haar wavelet transform, are presented. Then, the construction of the Scale and Haar wavelet funct

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8

Geetika Agotra and Prof. Manish Kumar Singhal. "A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 143–49. http://dx.doi.org/10.32628/ijsrst2411430.

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This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular

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9

Shehab Ahmed, Mohannad Abid, Haithem Abd Al-Raheem Taha, and Musab Tahseen Salah Aldeen. "Image Compression using Haar and Modified Haar Wavelet Transform." Tikrit Journal of Engineering Sciences 18, no. 2 (2011): 88–101. http://dx.doi.org/10.25130/tjes.18.2.08.

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Анотація:

Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and hori

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10

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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Анотація:

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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11

Christnatalis, Christnatalis, Bachtiar Bachtiar, and Rony Rony. "Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 202–9. http://dx.doi.org/10.31289/jite.v3i2.3154.

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In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used e

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12

Mrs., Yewale Varsharani Balaso *. Prof. Shinde S.S Prof. Tamboli S.S. "IMAGE COMPRESSION USING MODIFIED FAST HAAR WAVELET TRANSFORM (MFHWT)." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 8 (2016): 141–47. https://doi.org/10.5281/zenodo.59969.

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Анотація:

Image compression plays an important role in multimedia applications. It reduces memory requirements for storage of images. The most distinctive feature of Haar Transform lies in the fact that it lends itself easily to simple manual calculations. Modified Fast Haar Wavelet Transform (MFHWT), is one of the algorithms which can reduce the calculation work in Haar Transform (HT) and Fast Haar Transform (FHT). This project attempts to describe the algorithm for image compression using MFHWT. It includes a number of examples of different images to validate the utility and significance of algorithm&

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13

Khoromskij, Boris N., and Sentao Miao. "Superfast Wavelet Transform Using Quantics-TT Approximation. I. Application to Haar Wavelets." Computational Methods in Applied Mathematics 14, no. 4 (2014): 537–53. http://dx.doi.org/10.1515/cmam-2014-0016.

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Анотація:

AbstractWe propose a superfast discrete Haar wavelet transform (SFHWT) as well as its inverse, using the low-rank Quantics-TT (QTT) representation for the Haar transform matrices and input-output vectors. Though the Haar matrix itself does not have a low QTT rank approximation, we show that factor matrices used at each step of the traditional multilevel Haar wavelet transform algorithm have explicit QTT representations of low rank. The SFHWT applies to a vector representing a signal sampled on a uniform grid of size ${N=2^d}$. We develop two algorithms which roughly require square logarithmic

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14

NOVAMIZANTI, LEDYA, and ADRIAN KURNIA. "Analisis Perbandingan Kompresi Haar Wavelet Transform dengan Embedded Zerotree Wavelet pada Citra." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 3, no. 2 (2015): 161. http://dx.doi.org/10.26760/elkomika.v3i2.161.

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ABSTRAKKompresi data merupakan salah satu teknologi pemicu revolusi multimedia. Haar Wavelet mampu merepresentasikan ciri tekstur dan bentuk, sedangkan Embedded Zerotree Wavelet (EZW) mampu menyusun bit-bit menurut tingkat prioritas, sehingga mampu mencapai kompresi maksimal. Pada penelitian ini telah dilakukan perbandingan Haar Wavelet Transform dengan Embendded Zerotree Wavelet untuk kompresi citra. Pengujian menggunakan 4 citra grayscale berformat bitmap (.bmp) dengan resolusi 256x256 dan 512x512. Rasio Kompresi yang diperoleh dengan menggunakan algoritma Embedded Zerotree Wavelet dan Haar

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15

Iwaszkiewicz, J., and A. Muc. "Five-phase Inverter Control Based on the Haar Wavelet Transform." Renewable Energies, Environment and Power Quality Journal 2 (July 2024): 193–201. http://dx.doi.org/10.24084/reepqj24.380.

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The paper presents a novel five-phase cascade inverter control method based on the Haar wavelets transform. The inverter is built from two or three two level inverters in every phase. The instantaneous value of the output voltage of the cascaded inverter is composed as a synthesis of the selected Haar wavelets. An analytical system of determining the best possible set of Haar orthogonal wavelets is presented. On the basis of the applied Haar wavelets, the control signals of the inverter switches are calculated. The cascaded five-phase inverter with a star connected resistive-inductive load was

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16

Pathak, R. S., and Ashish Pathak. "Asymptotic Expansions of the Wavelet Transform for Large and Small Values ofb." International Journal of Mathematics and Mathematical Sciences 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/270492.

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Анотація:

Asymptotic expansions of the wavelet transform for large and small values of the translation parameterbare obtained using asymptotic expansions of the Fourier transforms of the function and the wavelet. Asymptotic expansions of Mexican hat wavelet transform, Morlet wavelet transform, and Haar wavelet transform are obtained as special cases. Asymptotic expansion of the wavelet transform has also been obtained for small values ofbwhen asymptotic expansions of the function and the wavelet near origin are given.

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17

Prevysokova, N. V. "Factorization of the matrices of discrete wavelet transform on the Galois functions base." Carpathian Mathematical Publications 15, no. 2 (2023): 543–51. http://dx.doi.org/10.15330/cmp.15.2.543-551.

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The paper deals with the factorization of the matrices of discrete wavelet transform based on the Galois functions of different orders. It is used the known method of factorization of the matrices of the discrete Haar transform. Factorized matrices of transforms are presented in the form of a product of sparse matrices. This representation is the basis for building fast transforms algorithms.

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18

ShantikumarSingh, Y., B. Pushpa Devi, and Kh Manglem Singh. "Image Compression with Haar Wavelet Transform." International Journal of Computer Applications 121, no. 9 (2015): 15–18. http://dx.doi.org/10.5120/21567-4603.

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19

Megson, G. M. "Systolic arrays for the Haar transform." IEE Proceedings - Computers and Digital Techniques 145, no. 6 (1998): 403. http://dx.doi.org/10.1049/ip-cdt:19982344.

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20

ALBANESI, M. G., and M. FERRETTI. "A HIGH SPEED HAAR TRANSFORM IMPLEMENTATION." Journal of Circuits, Systems and Computers 02, no. 03 (1992): 207–26. http://dx.doi.org/10.1142/s0218126692000143.

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This paper presents an implementation of the Haar transform suitable for VLSI integration. It shows how to map a bidimension linear transformation, which has a straightforward multiresolution realization on a pyramid data-parallel computer, onto a pipeline of simple processors. A further simplification of the linear structure leads to an extremely simple implementation based on a two-stage pipeline, capable of processing images as large as 1024×1024 pixels. VLSI simulations with current technologies predict HDTV video rates. Data compression is among the applications that benefit from the new

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21

Ray Liu, K. J. "VLSI computing architectures for Haar transform." Electronics Letters 26, no. 23 (1990): 1962. http://dx.doi.org/10.1049/el:19901269.

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22

Ibrahim, Ali Abdulmunim. "Iris Recognition using Haar Wavelet Transform." Journal of Al-Nahrain University Science 17, no. 1 (2017): 180–86. http://dx.doi.org/10.22401/jnus.17.1.25.

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23

Morosetti, Stefano. "Discrete Haar Transform and Protein Structure." Journal of Biomolecular Structure and Dynamics 15, no. 3 (1997): 489–97. http://dx.doi.org/10.1080/07391102.1997.10508960.

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24

Kumar, Anil, and Meenu Kumari. "Face Identification Using HAAR Wavelet Transform." International Journal of Advance Research and Innovation 8, no. 1 (2020): 8–11. http://dx.doi.org/10.51976/ijari.812002.

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Анотація:

Face identification is the process of matching one or more people by analyzing and comparing the patterns of their faces. Algorithms for face identification typically extract facial features and compare them to a database to find the best match. The Haar wavelet transform has been mainly used for image processing and pattern identification due to its low computing requirements and quality to conserve and to compact the energy of a signal. In discrete wavelet transform, an image signal can be analyzed by passing it through an analysis filter bank followed by a decimation operation. Face identif

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25

Salijanovich, Ibragimov Sanjarbek. "Piecewise Polynomial Methods of Haar Transform in Digital Signal Processing." European International Journal of Pedagogics 5, no. 5 (2025): 96–103. https://doi.org/10.55640/eijp-05-05-21.

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This paper analyzes the mathematical foundations and practical significance of piecewise polynomial methods based on the Haar orthogonal basis in the process of digital signal processing. Algorithms for calculating spectral coefficients in Haar, Schauder, and spline bases are compared, and their structural and computational efficiency is presented through graphs and formulas. In particular, the advantages of fast transform algorithms adapted for piecewise-constant, piecewise-linear, and piecewise-quadratic bases are demonstrated, along with the challenges encountered during their implementatio

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26

Vani, S., G. R. Suresh, T. Balakumaran, and Cross T. Ashawise. "EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1301–6. http://dx.doi.org/10.1166/jmihi.2019.2713.

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Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process

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27

FALKOWSKI, BOGDAN J., RADOMIR S. STANKOVIĆ, and DRAGAN JANKOVIĆ. "MINIMIZATION OF CIRCUIT DESIGN USING PERMUTATION OF HAAR WAVELET SERIES." Journal of Circuits, Systems and Computers 14, no. 03 (2005): 483–95. http://dx.doi.org/10.1142/s0218126605002453.

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The paper discusses complexity of circuit realization through Haar wavelet series. The efficiency in circuit synthesis using a method for minimization of the number of non-zero Haar coefficients by permutation of binary coordinates of indices of Haar functions is considered. Some applications of Haar wavelet transform and efficient ways of its calculation for logic domain are also discussed.

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28

Richter, G. M., M. Capaccioli, G. Longo, and H. Lorenz. "Data Compression and Wavelet Transforms." Symposium - International Astronomical Union 161 (1994): 219–23. http://dx.doi.org/10.1017/s0074180900047355.

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Анотація:

Efficient data compression needs analyzing functions to recognise the local resolution of the signal. They are provided by the wavelet concept. The optimal wavelet (best information concentration) is defined by the image model of the application. For the most common images in astronomy the H-transform is optimal (in the sense of Karhunen-Loeve transform). The role of the H-transform in 2-dimensional processing is the same as the Haar-transform in 1-dimension, but it is not the ‘2-dimensional Haar-transform’ found in text books.

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29

Mr., Yashawant S. Jagadale*1 Mr Kuldeep P. Pawar2 &. Mr. Digvijay J. Pawar3. "ANALYSIS OF COLOR IMAGE COMPRESSION USING WAVELET TRANSFORM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 621–26. https://doi.org/10.5281/zenodo.817978.

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Images have considerably higher data storage requirement than text .This paper gives compression of color image by using Haar Wavelet Transform and 3D wavelet transform techniques for various size with respect to the parameters such as compression ratio, mean square error and peak signal to noise ratio respectively and observe that these parameters shows better performance and superior image quality in Haar wavelet than 3D wavelet.

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30

Saito, Naoki, and Yiqun Shao. "eGHWT: The Extended Generalized Haar–Walsh Transform." Journal of Mathematical Imaging and Vision 64, no. 3 (2022): 261–83. http://dx.doi.org/10.1007/s10851-021-01064-w.

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AbstractExtending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose the extended generalized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT exami

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31

Manoj Chavan. "Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier." Journal of Electrical Systems 20, no. 4s (2024): 2453–62. http://dx.doi.org/10.52783/jes.2798.

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verific

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32

Caso, Gregory, and C. C. Jay Kuo. "Multiresolution Analysis of Fractal Image Compression." Fractals 05, supp01 (1997): 215–29. http://dx.doi.org/10.1142/s0218348x97000772.

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In this research, we perform a multiresolution analysis of the mappings used in fractal image compression. We derive the transform-domain structure of the mappings and demonstrate a close connection between fractal image compression and wavelet transform coding using the Haar basis. We show that under certain conditions, the mappings correspond to a hierarchy of affine mappings between the subbands of the transformed image. Our analysis provides new insights into the mechanism underlying fractal image compression, leads to a new non-iterative transform-domain decoding algorithm, and suggests a

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33

Kaur, Khushpreet, and Sheenam Malhotra. "Image Compression using HAAR Wavelet Transform and Discrete Cosine Transform." International Journal of Computer Applications 125, no. 11 (2015): 28–31. http://dx.doi.org/10.5120/ijca2015906141.

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34

Chen, Dong, Yanjuan Li, Jiaquan Chen, Hongbo Bi, and Xiajun Ding. "Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range Query." Computational Intelligence and Neuroscience 2022 (September 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/8139813.

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Анотація:

Range query is the hot topic of the privacy-preserving data publishing. To preserve privacy, the large range query means more accumulate noise will be injected into the input data. This study presents a research on differential privacy for range query via Haar wavelet transform and Gaussian mechanism. First, the noise injected into the input data via Laplace mechanism is analyzed, and we conclude that it is difficult to judge the level of privacy protection based on the Haar wavelet transform and Laplace mechanism for range query because the sum of independent random Laplace variables is not a

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35

Park, Ju Yong, Md Hashem Ali Khan, Jeong Su Kim, and Moon Ho Lee. "Inverse-Orthogonal Jacket-Haar and DCT Transform." Journal of the Institute of Electronics and Information Engineers 51, no. 9 (2014): 30–40. http://dx.doi.org/10.5573/ieie.2014.51.9.030.

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36

Narula, Swati, and Sunanada Gupta. "Image Compression Radiography using Haar Wavelet Transform." International Journal of Computer Applications 117, no. 18 (2015): 14–15. http://dx.doi.org/10.5120/20653-3187.

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37

Wu, C. Y., A. R. D. Somervell, T. G. Haskell, and T. H. Barnes. "Optical Mellin transform through Haar wavelet transformation." Optics Communications 227, no. 1-3 (2003): 75–82. http://dx.doi.org/10.1016/j.optcom.2003.09.040.

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38

Murtagh, Fionn. "The Haar Wavelet Transform of a Dendrogram." Journal of Classification 24, no. 1 (2007): 3–32. http://dx.doi.org/10.1007/s00357-007-0007-9.

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39

Falkowski, Bogdan J., and Chip-Hong Chang. "Properties and calculation of Paired Haar transform." Approximation Theory and its Applications 15, no. 2 (1999): 1–14. http://dx.doi.org/10.1007/bf02836788.

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40

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

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Анотація:

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 ver

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Stanimirovic, Zdravko, Ivanka Stanimirovic, Slobodanka Galovic, Katarina Djordjevic, and Edin Suljovrujic. "Haar wavelet operational matrix based numerical inversion of Laplace transform for irrational and transcendental transfer functions." Facta universitatis - series: Electronics and Energetics 36, no. 3 (2023): 395–410. http://dx.doi.org/10.2298/fuee2303395s.

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Irrational and transcendental functions can often be seen in signal processing or physical phenomena analysis as consequences of fractional-order and distributed order models that result in fractional or partial differential equations. In cases when finding solution in analytical form tends to be difficult or impossible, numerical calculations such as Haar wavelet operational matrix method can be used. Haar wavelet establishes a direct procedure for transfer function inversion using the wavelet operational matrix for orthogonal function set integration. In this paper an inverse Laplace transfo

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ÜSTÜNDAĞ, Özgür, and Erdal DINÇ. "Continuous wavelet transform approach for the assay of active components in tablets." Revue Roumaine de Chimie 67, no. 8-9 (2022): 447–54. http://dx.doi.org/10.33224/rrch.2022.67.8-9.03.

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Two continuous wavelets transform (CWT) methods were developed for the simultaneous determination of candesartan cilexetil (CAN) and hydrochlorothiazide (HCT) in tablets. The CWT methods were applied to the ratio spectra (RS) of the CAN and HCT. The CWT approaches based on the application of Haar and Mexican hat function (RS-HAAR-CWT and RS-MEXH-CWT, respectively) to the transformation of the ratio spectra were found to be suitable to reach a good spectral quantification. In the use of transformed ratio spectra, the calibration graphs for CAN and HCT were computed by using the RS-CWT-amplitude

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H, Soumya Babu, Vijayakumar N, and Gopakumar K. "Discrete Wavelet Transform based Cryptosystem." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (2022): 47–52. http://dx.doi.org/10.17762/ijritcc.v10i11.5779.

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In this article, the authors proposed, implemented and analysed a symmetric key cryptographic algorithm that can be considered as a lossless encryption and decryption technique, advantageous especially in situations where, even a slight marginal distortion is not tolerable. In the proposed system, Haar wavelet is used initially, to transform the original target image into its frequency domain, followed by encrypting the resulting sub-bands, so as to obtain a secure and reliable encrypted image. The resulting coefficients after Haar decomposition is scattered using a reversible weighing factor,

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Hou, Yingkun, Xiaobo Qu, Guanghai Liu, Seong-Whan Lee, and Dinggang Shen. "Block-Extraction and Haar Transform Based Linear Singularity Representation for Image Enhancement." Mathematical Problems in Engineering 2019 (August 6, 2019): 1–14. http://dx.doi.org/10.1155/2019/6395147.

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In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching a

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Song, Feijun. "Detection of central positions of noisy rounded square function using modified Haar wavelet transform and Haar-Gaussian wavelet transform." Optical Engineering 39, no. 5 (2000): 1190. http://dx.doi.org/10.1117/1.602491.

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Wei, Qing, Hao Zhang, and Zhi Jing Liu. "Activity Recognition via Feature Decomposition." Advanced Materials Research 243-249 (May 2011): 6221–24. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.6221.

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This paper presents a new recognition method for human motion, which is represented by Haar wavelet transform and recognized by Coupled Hidden Markov Model. We tackle the challenge of detecting the feature points by Haar wavelet transform to improve the accuracy. We extract binary silhouette after creating the background model. Then the low-level features are detected by Haar wavelet and principal vectors in two subspaces are obtained. We utilize Coupled Hidden Markov Models to model and recognize them, and demonstrate their usability. Compared with others, our approach is simple and effective

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Liu, Tie Jun, and Yin Jia Chen. "De-Noising Signal of Electromagnetic Flowmeter Based on Wavelet Transform." Applied Mechanics and Materials 220-223 (November 2012): 2144–49. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2144.

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A Software filter technique based on Haar wavelet is proposed for effectively suppressing the noise components in the signal of electromagnet flow transducer. It is proved with computer simulation based on MATLAB Wavelet Toolbox that the proposed filter technique is well matched with the feature of the signal of the electromagnetic flow transducer in effectiveness and efficiency. A prototype flowmeter was developed based on the proposed technique, experimental result with the prototype shows that the proposed Haar wavelet filter technique is well suitable for de-noising the signal of electroma

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Bespalov, M. S. "Wavelet p-analogs of the discrete Haar transform." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 21, no. 4 (2021): 520–31. http://dx.doi.org/10.18500/1816-9791-2021-21-4-520-531.

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Ma, Cui Hong, Yi Li, and Ying Wang. "Image Analysis Based on the Haar Wavelet Transform." Applied Mechanics and Materials 391 (September 2013): 564–67. http://dx.doi.org/10.4028/www.scientific.net/amm.391.564.

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Wavelet is a kind of mathematics tool rapid development in recent years and widely used in various areas of technology, Wavelet analysis of image processing is the most widely used and mature areas. Wavelet change based on the features, Using Matlab software, this paper analyses the wavelet in image decomposition, denoising, compression, reconstruction, etc . Concluded that the wavelet transform for image processing have ideal effect.

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Zhu Wen, Yao, Jun Zhou, Yu feng Wu, and Ming Jun Wang. "Iris Feature Extraction based on Haar Wavelet Transform." International Journal of Security and Its Applications 8, no. 4 (2014): 265–72. http://dx.doi.org/10.14257/ijsia.2014.8.4.24.

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