Restoration of images corrupted by Gaussian and uniform impulsive noise

López‐Rubio, Ezequiel

doi:10.1016/j.patcog.2009.11.017

Cited by 86 publications

(41 citation statements)

References 47 publications

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“…In order to restore the image corrupted by the mixed noise, many methods have been developed [21][22][23][24][25][26]. The trilateral filter (TF) [22] incorporates the rankorder absolute difference statistics into BF framework for IN detection.…”

Section: Introductionmentioning

confidence: 99%

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Nie

2018

Mathematical Problems in Engineering

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A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

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Section: Introductionmentioning

confidence: 99%

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Nie

2018

Mathematical Problems in Engineering

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“…The suppression of the disturbances introduced by the impulsive noise is indispensable for the success of further stages of the image processing pipeline [7][8][9][10][11][12] and, therefore, we present a novel, very fast denoising algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast averaging peer group filter for the impulsive noise removal in color images

Maliński

Smołka

2015

J Real-Time Image Proc

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In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.

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“…In [20], a method for the restoration of heavily damaged images, in which Bayesian classification of the input pixels is combined with the kernel regression framework, was proposed. In [46], similarly to the ROAD statistic, but for color images, a sum of distances to the k-nearest pixels is used to calculate the weights for each pixel that can be treated as a measure of pixel distortion and these coefficients are used in the weighted average of the pixels in the processing window.…”

Section: Introductionmentioning

confidence: 99%

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

Smołka

Kusnik

2015

SIViP

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In this paper, a novel technique designed for the suppression of mixed Gaussian and impulsive noise in color images is proposed. The new denoising scheme is based on a weighted averaging of pixels contained in a filtering block. The main novelty of the proposed solution lies in the new definition of the similarity between the samples of the processing block and a small window centered at the block's central pixel. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the neighborhood of the central pixel is utilized. This measure is defined as the sum of distances in a given color space, between a pixel of the block and a certain number of most similar samples from the filtering window. The main advantage of the proposed scheme is that the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing mixed noise of high intensity and is competitive with respect to the stateof-the-art noise filtering methods.

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Restoration of images corrupted by Gaussian and uniform impulsive noise

Cited by 86 publications

References 47 publications

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Fast averaging peer group filter for the impulsive noise removal in color images

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

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