Image denoising using wavelet transform and median filtering

Boyat, Ajay; Joshi, Brijendra Kumar

doi:10.1109/nuicone.2013.6780128

Cited by 43 publications

(21 citation statements)

References 9 publications

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“…This paper is an extension of [14]. We present an integration of wavelet transform and median filter.…”

Section: Proposed Methodsmentioning

confidence: 99%

Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

Joshi¹,

Boyat²,

Joshi³

2014

2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

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Image ac q uisition is a common task in ever y image processing operation. Noise is entered during image ac q uisition from its source and once entered it degrades the image and is difficult to remove. In order to achieve the noise cancellation in an image, non-linear filter works better than linear. This paper presents the joint scheme of Wavelet Transform using iterative noise densit y and Median Filtering to remove Salt and Pepper Noise in Digital Images. The first part of the paper derives the wavelet coefficients with slight increase in noise densit y and in second part these coefficients are further modified b y median filter. The algorithm shows the remarkable improvement over Gaussian noise model and removes most of the nois y part from the image and maintains the visual q ualit y . The level of wavelet decomposition is restricted to three. The renowned indexes Peak Signal to Noise Ratio (PSNR) and Root Mean S q uare Error (RMSE) demonstrate marked improvement of image denoising over Gaussian method.

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“…This paper is an extension of [14]. We present an integration of wavelet transform and median filter.…”

Section: Proposed Methodsmentioning

confidence: 99%

Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

Joshi¹,

Boyat²,

Joshi³

2014

2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

Self Cite

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“…It is also called as electronic noise because it arises in amplifiers or detectors. Gaussian noise caused by natural sources such as thermal vibration of atoms and discrete nature of radiation of warm objects [5].…”

Section: Gaussian Noise Modelmentioning

confidence: 99%

A Review Paper : Noise Models in Digital Image Processing

Boyat¹,

Joshi²

2015

SIPIJ

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278

115

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“…The noise in an image may transpire from analog circuitry as electronic noise, photon noise, thermal agitation of ions, coding errors, etc, [11]. Blurring is a kind of noise that may occur because of improper camera setting or when the image has been captured from a moving platform [12]. To examine image denoising techniques, it is essential to have prior knowledge of noise models.…”

Section: Introductionmentioning

confidence: 99%

Efficient Denoising by Feature Recovery from Residual Noise in Spatial Domain

Girdher*,

Goyal,

Dogra

et al. 2019

IJRTE

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The growth of image processing techniques has lead to limitless applications of imaging in various fields of medical, remote sensing. However, the unpreventable problem of noise contamination arises during the processing of images. As the noise content in an image rises, it becomes difficult to denoise an image while preserving high-frequency edge features as well as low frequency smooth features. Minimal artefacts and better preservation of geometrical details such as edges and texture reflects efficient image reconstruction. Since many State-of-theart denoising algorithms have been reported in the literature, and there is always a compromise between noise removal and feature preservation. A novel approach for efficient noise removal along with recovery of fine features is being proposed. The idea behind denoising approach is the use of hybridization of spatial domain filters where base layer image and residual image are extracted and processed separately to mitigate the prevalence of artefacts and preserve image content. The performance of the proposed method is evaluated both quantitatively as well as qualitatively,and it is found that the proposed method could outperform existing denoising techniques.

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Image denoising using wavelet transform and median filtering

Cited by 43 publications

References 9 publications

Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

A Review Paper : Noise Models in Digital Image Processing

Efficient Denoising by Feature Recovery from Residual Noise in Spatial Domain

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