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Journal articles on the topic 'VisuShrink'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Chaabouni, Imen, Wiem Fourati, and M. Salim Bouhlel. "Visushrink Pretreatment for Image Compression." International Journal of Computer Applications 23, no. 6 (2011): 10–16. http://dx.doi.org/10.5120/2893-3785.

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2

Chikanbanjar, Milan. "Comparative analysis between non-linear wavelet based image denoising techniques." Journal of Science and Engineering 5 (August 31, 2018): 58–67. http://dx.doi.org/10.3126/jsce.v5i0.22373.

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Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) an

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3

Xie, Wendi, Yurong Zhao, and Yang Wang. "Research on Wavelet Transform Technology and Image Processing Performance Based on Matlab." Journal of Physics: Conference Series 2031, no. 1 (2021): 012007. http://dx.doi.org/10.1088/1742-6596/2031/1/012007.

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Abstract This paper first introduces the basic concept, principle and application of wavelet transform. Then, the performance characteristics of Haar wavelet, dbN wavelet and symN wavelet, two different threshold shrinkage algorithms Visushrink and Bayesshrink, and two common threshold functions are described in detail. Finally, Matlab software programming is used to realize the simulation of the original image based on different wavelet bases in the influence of Gaussian noise, Poisson noise and salt and pepper noise, different threshold acquisition algorithm, different threshold function, an

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4

CHEN, GUANGYI, TIEN D. BUI, and ADAM KRZYZAK. "DENOISING OF THREE-DIMENSIONAL DATA CUBE USING BIVARIATE WAVELET SHRINKING." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 03 (2011): 403–13. http://dx.doi.org/10.1142/s0218001411008725.

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The denoising of a natural signal/image corrupted by Gaussian white noise is a classical problem in signal/image processing. However, it is still in its infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick's bivariate wavelet thresholding from two-dimensional (2D) image denoising to three-dimensional (3D) data cube denoising. Our study shows that bivariate wavelet thresholding is still valid for 3D data cubes. Experimental results show that bivariate wavelet thresholding on 3D data cube is better than performing 2D bivariate wavelet thresholding on every sp

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5

Golkar Amoli, Mehdi, Mahdi Hasanlou, Ruhollah Taghizadeh Mehrjardi, and Farhad Samadzadegan. "Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran." Remote Sensing 16, no. 12 (2024): 2149. http://dx.doi.org/10.3390/rs16122149.

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Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoisi

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6

Nasar, Iqbal. "Reduction of Noise from Fingerprint Images using Stationary Wavelet Trasnform." International Journal of Engineering Works (ISSN: 2409-277) 4, no. 12 (2017): 104–8. https://doi.org/10.5281/zenodo.1133286.

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In Automatic Fingerprint Identification Systems (AFIS) the quality of image is a very important factor as the minutiae extraction from fingerprint image heavily depends on image quality. To enhance the quality of fingerprint images a large number of denoising methods has been used. In this paper fingerprint image enhancement using stationary wavelet transform has been analyzed using different wavelets with different thresholds. Four different wavelets namely Haar DB4 (Daubechies), Coif2 (Coilflets) and Bior1.3 (Biorthogonal) were selected with four thresholds namely VisuShrink, NormalShrink, N

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7

Kumar, Sushil. "DT-CWT Based Block Nonlinear De-Noising." International Journal of Advance Research and Innovation 4, no. 2 (2016): 6–13. http://dx.doi.org/10.51976/ijari.421602.

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For image denoising, Block thresholding is considered to be a better strategy than the term-by-term thresholding. There are number of Wavelet based thresholding techniques for image denoising such as VisuShrink, BayesShrink, SureShrink, NeighShrink, BiShrink, ProbShrink, Sure-LET and BlockShrink. Selesnick has extended the BiShrink method to DT-CWT and he has shown that the DT-CWT achieves better results than DWT for image denoising. Dengwen and Xiaoliu have shown that BlockShrink enjoys a number of advantages over the other conventional image denoising methods. Their experimental results show

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8

Nisha, S. Shajun, and S. P. Raja. "Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation." Cybernetics and Information Technologies 20, no. 3 (2020): 130–46. http://dx.doi.org/10.2478/cait-2020-0033.

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AbstractDue to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image

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9

Seetharaman, R., M. Tharun, and K. Anandan. "A Novel approach in Hybrid Median Filtering for Denoising Medical images." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1757-899x/1187/1/012028.

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Abstract Image denoising is a key pre-processing step in medical image analysis. At current, deep learning-based models have shown a great promise, which outperformed many conventional methods over the past three decades. Speckle noise removal is a major issue in preserving all the delicate details and the edges in ultrasound image processing as it degrades the visual evaluation of ultrasound images. The multiplicative behavior of speckle-noise is converted into additive by using log transform as the additive noise removal is easy as compared to multiplicative noise. An innovative approach for

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10

Gong, Tierui, Zhijia Yang, Gengshan Wang, and Ping Jiao. "Supervised and Unsupervised Subband Adaptive Denoising Frameworks with Polynomial Threshold Function." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5203214.

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Unlike inflexible structure of soft and hard threshold function, a unified linear matrix form with flexible structure for threshold function is proposed. Based on the unified linear flexible structure threshold function, both supervised and unsupervised subband adaptive denoising frameworks are established. To determine flexible coefficients, a direct mean-square error (MSE) minimization is conducted in supervised denoising while Stein’s unbiased risk estimate as a MSE estimate is minimized in unsupervised denoising. The SURE rule requires no hypotheses or a priori knowledge about clean signal

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11

Xie, Zhi Jie, Bao Yu Song, Yang Zhang, and Feng Zhang. "Application of an Improved Wavelet Threshold Denoising Method for Vibration Signal Processing." Advanced Materials Research 889-890 (February 2014): 799–806. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.799.

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Vibration signal analysis has been widely used in the fault detection and condition monitoring of rotation machinery. But the practical signals are easily polluted by noises in their transmission process. The raw signals should be processed to reduce noise and improve the quality before further analyzing. In this paper an improved wavelet threshold denosing method for vibration signal processing is proposed. Firstly, a new threshold is developed based on the VisuShrink threshold. The effect of noise standard deviation and wavelet coefficient is retained, and the correlation of wavelet decompos

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12

James, Julian, Bagas Dewantara Annastya, Winarta Adi, and Putra Nandy. "Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2625–33. https://doi.org/10.11591/ijece.v14i3.pp2625-2633.

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Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase

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13

Chikanbanjar, Milan, and Sanjeeb Pandey. "Comparative Study on Wavelet-based Linear and Non-Linear Image Denoising Techniques." Journal of Science and Technology 4, no. 1 (2024): 1–6. https://doi.org/10.3126/jost.v4i1.74554.

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Denoising an image is still a challenging domain in the image processing research area. Denoising means removing noise from a corrupted image but the challenge is to retain different details of an image. The search for efficient image-denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recent methods, most algorithms have not yet attained a desirable level of applicability. All the algorithms show a high outstanding performance when the image model corresponds to the algorithm assumptions but it generally fai

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14

Wang, Xuhui, and Jizhong Zhao. "Adaptive Threshold Wavelet Denoising Method and Hardware Implementation for HD Real-Time Processing." Electronics 14, no. 11 (2025): 2130. https://doi.org/10.3390/electronics14112130.

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To meet the demands of real-time and high-definition (HD) image processing applications, denoising methods must be both computationally efficient and hardware friendly. Traditional image denoising techniques are typically simple, fast, and resource-efficient but often fall short in terms of denoising performance and adaptability. This paper proposes an adjustable-threshold denoising method along with a corresponding hardware implementation designed to support the real-time processing of large-array images commonly used in image signal processors (ISPs). The proposed technique employs a LeGall

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15

D.Gnanadurai and V.Sadasivam. "An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising." International Journal of Electrical, Electronic and Communication Sciences 1.0, no. 8 (2008). https://doi.org/10.5281/zenodo.1328672.

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This frame work describes a computationally more efficient and adaptive threshold estimation method for image denoising in the wavelet domain based on Generalized Gaussian Distribution (GGD) modeling of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analysing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficie

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16

R., Senthilkumar. "Performance Improvement in the Bivariate Models by using Modified Marginal Variance of Noisy Observations for Image-Denoising Applications." November 28, 2007. https://doi.org/10.5281/zenodo.1063062.

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Most simple nonlinear thresholding rules for wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper compares different Shrinkage functions used for image-denoising. The second part of the paper compares di

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17

Ravisankar, Priyadharsini. "Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 2 (2021). http://dx.doi.org/10.5565/rev/elcvia.1360.

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Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shr

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