Image Denoising Based on Wavelet Transform using Visu Thresholding Technique

Koranga, Pushpa; Singh, Garima; Verma, Dikendra; Chaube, Shshank; Kumar, Anuj; Pant, Sangeeta

doi:10.33889/ijmems.2018.3.4-032

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…Two measurement parameters were used to calculate the noise in the image, which are (PSNR, RMSE) for various wavelet filters as Db1, Bior1.5, Sym1, and coif1. The results presented an improved performance in eliminating the Gaussian noise from the degraded image and achieved better visual quality [27].…”

Section: Wavelet Domain Techniquementioning

confidence: 96%

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

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Section: Wavelet Domain Techniquementioning

confidence: 96%

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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“…The wavelet coefficients in the wavelet domain are softer after soft thresholding, and the reconstructed image also shows more inferior when determined with the hard thresholding technique. Soft thresholding is represented in the equation below [Koranga, 2018].…”

Section: Soft Thresholdmentioning

confidence: 99%

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Mustafa

2022

ACAD J NAWROZ UNIV

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In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance. Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques. In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.

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“…In this work, the multiresolution approach applied with two analyses. In order to obtain less distortion in the image, a suitable threshold value must be used (Koranga, 2018;Ergen, 2012;Qian, 2015).…”

Section: Thresholding Techniquementioning

confidence: 99%

“…Image enhancement methods can be considered a perfect part of image processing which is in demand in many fields such as weather forecasting, astrophysics, medical fields, and so on. Recovering the original image from the noisy image, thus reducing the loss of information, can be considered as actual gain (Bhargava, 2018;Koranga, 2018). Also, noise can be defined as the undesirable by-product of image capture that obscures the desired information.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement in wavelet domain based on histogram equalization and median filter

Abdullah

Mustafa

Elçi

2021

JER is an international, peer-reviewed journal that publishes f

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During the acquisition of a new digital image, noise may be introduced as a result of the production process. Image enhancement is used to alleviate problems caused by noise. In this work, the purpose is to propose, apply, and evaluate enhancement approaches to images by selecting suitable filters to produce improved quality and performance results. The new method proposed for image noise reduction as an enhancement process employs threshold and histogram equalization implemented in the wavelet domain. Different types of wavelet filters were tested to obtain the best results for the image noise reduction process. Also, the effect of canceling one or more of the high-frequency bands in the wavelet domain was tested. The mean square error and peak signal to noise ratio are used for measuring the improvement in image noise reduction. A comparison made with two related works shows the superiority of the methods proposed and implemented in this research. The proposed methods of applying the median filter before and after the histogram equalization methods produce improvement in performance and efficiency compared to the case of using discrete wavelet transform only, even with the cases of multiresolution discrete wavelet transform and the cancellation step.

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Image Denoising Based on Wavelet Transform using Visu Thresholding Technique

Cited by 13 publications

References 18 publications

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Image enhancement in wavelet domain based on histogram equalization and median filter

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