Gaussian noise removal in gray scale images using fast Multiscale Directional Filter Banks

Leavline, E. Jebamalar; Sutha, S.

doi:10.1109/icrtit.2011.5972377

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Many image denoising methods have been proposed to remove Gaussian and impulse noise separately or together. However, they may remove high frequency fea-tures from the images, especially when they are corrupted with the high level of noise(s) [22,23,24]. Another drawback in some of MDC methods is that DWT or DCT is used as the transformation domain which can not represent sufficient orientations of the images.…”

Section: Survey Of MDC Methodsmentioning

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: Survey Of MDC Methodsmentioning

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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“…The resultant images at various stages of FMDFB are shown in Figure 15. The number of scale (s) and directional (l) decomposition is restricted to 2 [19].The original image of size 256 × 256 is fed as input to the FMDFB. At the first scale decomposition, the image is decomposed into coarse (low-pass) and detailed (band-pass) images of size 128 × 128 and 256 × 256 respectively.…”

Section: Discussionmentioning

confidence: 99%

Design of FIR Filters for Fast Multiscale Directional Filter Banks

Leavline¹,

Sutha²

2014

IJUNESST

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“…High levels of noise are always undesirable. Hence noise removal has to be employed before the image could be used for further analysis [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Median Filtering of Salt and Pepper Noise based on Local Pixel Distribution

Gao¹

2018

Proceedings of the 2018 International Conference on Transportation &Amp; Logistics, Information &Amp; Communication, Smart City

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A selective adaptive median filter is proposed for the restoration of gray scale images that are highly corrupted by salt and pepper noise. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. After detecting the salt and pepper noise pixels, the self-adaptive median filter is use to find a suitable window containing more non-noise pixels. This proposed algorithm provides much better results than that of the standard median filter, weighed median filter and switching mean median filter. The proposed algorithm is tested against different gray scale images and it gives better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM).

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Gaussian noise removal in gray scale images using fast Multiscale Directional Filter Banks

Cited by 8 publications

References 10 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

Design of FIR Filters for Fast Multiscale Directional Filter Banks

An Adaptive Median Filtering of Salt and Pepper Noise based on Local Pixel Distribution

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