Color image denoising using wavelets and minimum cut analysis

Lian, Nai-Xiang; Zagorodnov, Vitali; Tan, Yap‐Peng

doi:10.1109/lsp.2005.856865

Cited by 19 publications

(5 citation statements)

References 15 publications

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“…The wavelet transformation methods have been widely used to resolve many image processing problems. For instance, they are used in denoising [26], edge detection [27], feature extraction [28], speech recognition [29], biomedical imaging [30], image compression and image resolution segmentation [31,32] and others [33,34]. Besides this, many MDC generation schemes have been using DWT to create the MDCs [6,13].…”

Section: Wavelet Transformation For MDC Generationmentioning

confidence: 99%

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

Kusetoğulları

Yavariabdi

2018

Applied Soft Computing

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A multiobjective optimization algorithm (MOEA) has been adapted to optimize two different objective functions and find Pareto solutions.2. The MOEA is integrated with Dual-Tree Complex Wavelet Transform (DT-CWT) to provide effective multimedia communication in lossy networks.3. The DT-CWT is used to obtain the subbands or set of coefficients which are used as a search space in the optimization problem. One fitness function is designed to generate optimal multiple description coding (MDCs) ordescriptions and the second one is used to obtain optimal parameter values for denoising filter to reduce mixed noise on descriptions.5. Proposed adaptive MDC system can be applied to the corrupted mixed noisy image (e.g. Gaussian and Impulse noise).

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Section: Wavelet Transformation For MDC Generationmentioning

confidence: 99%

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

Kusetoğulları

Yavariabdi

2018

Applied Soft Computing

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“…In image processing based applications, image compression, image denoising and image watermarking are at the cutting edge, and as such, a brief description of these wavelet-based applications is given in the following subsections (Strang & Nguyen, 1996;Burrus et al, 1998;Stromme, 1999;Ebrahimi et al, 2002;Nibouche et al, 2000Nibouche et al, , 2001aNibouche et al, , 2001bNibouche et al, , 2001cNibouche et al, , 2001dNibouche et al, , 2002Nibouche et al, , 2003Smith, 2003;Do & Vetterli, 2003Hankerson et al, 2005;Nai-Xiang Yap-Peng, 2005;Xiong & Ramchandran, 2005;Chappelier & Guillemot, 2006;Cunha et al, 2006;Nai-Xiang et al, 2006;Raviraj & Sanavullah, 2007;Hernandez-Guzmane et al, 2008;Firoiu et al, 2009;Mallat, 2009;Brislawn, 2010;Oppenheim & Schafer, 2010;Ruikar & Doye, 2010& Chen & Qian, 2011.…”

Section: Wavelet-based Applicationsmentioning

confidence: 99%

“…Hence, to recover the original structure of the image, the undesired added noise needs to be localised and then removed. In image processing, noise removal is achieved through the usage of filtering-based denoising techniques (Nai-Xiang & Yap-Peng, 2005;Chappelier & Guillemot, 2006;Firoiu et al, 2009;Mallat, 2009;Nafornita et al, 2009;Ruikar & Doye, 2010;Oppenheim & Schafer, 2010& Chen & Qian, 2011. Traditionally, image denoising or image enhancement is performed using either linear filtering or non-linear filtering.…”

Section: Image Denoisingmentioning

confidence: 99%

The Wavelet Transform for Image Processing Applications

Bouden¹,

Mokhtar²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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“…This is an undesirable property, making DWT not well suited for some applications like color image denoising more accurately. The shift-sensitivity of the DWT is a consequence of the aliasing introduced by the down-samplers followed by up-samplers from the DWT's filter-bank implementation [10,21].…”

Section: Introductionmentioning

confidence: 99%

“…The characteristic of edges and textures has the great significance for the visual perception of a color image [10,16]. So the color image features preserving is necessary during its denoising.…”

Section: Introductionmentioning

confidence: 99%

An Improved Color Image Denoising using Spatially Adaptive Statistical Dependency in Undecimated Wavelet Transform Coefficients

Paul,

Singh,

Saini

2024

Preprint

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In color image denoising, it is very important to preserve the edge and color information of the images. The standard discrete wavelet transforms provide a means of implementing a multiscale analysis, based on a critically sampled filter bank with perfect reconstruction, but shift-variance and directional selectivity is not supported by them which are necessary for color image processing. This is due to the decimation used in traditional wavelet transform and can be covered by undecimated wavelet transform. An adaptive color image denoising algorithm using spatial correlation in undecimated wavelet transform is proposed in this paper that preserves the color image features more efficiently. The noisy color image is first decomposed into different subbands of frequency and orientation responses using the undecimated wavelet transform. Then the spatial correlation of wavelet coefficients in a noisy color image is achieved by the coefficients multiplication of adjacent decomposition levels. The threshold value is calculated from the spatially correlated wavelet coefficients by computing the noise standard deviation from the correlated diagonal subband of first and second decomposition level. Finally, all noisy spatially correlated wavelet coefficients are relatively well denoised by soft thresholding. The experimental results demonstrate that the proposed color image denoising algorithm preserve the edges and color components very well while reducing noise. The efficiency and performance of this algorithm is compared with state-of-art denoising algorithms based on Peak Signal to Noise Ratio and visual perception.

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Color image denoising using wavelets and minimum cut analysis

Cited by 19 publications

References 15 publications

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

The Wavelet Transform for Image Processing Applications

An Improved Color Image Denoising using Spatially Adaptive Statistical Dependency in Undecimated Wavelet Transform Coefficients

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