An impulsive noise color image filter using learning-based color morphological operations

Zhou, Hao; Mao, Kezhi

doi:10.1016/j.dsp.2007.04.013

Cited by 25 publications

(10 citation statements)

References 41 publications

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“…For a corruption by impulsive noise with noise ratio p, only p of the total pixels is replaced with the impulse and the others are kept free. In this work, the noise model can be described as follows: 20,21 x͑k͒ = ͭ s͑k͒ with probability 1 − p n͑k͒ with probability p , ͑16͒…”

Section: Simulations On the Test Images 41 Impulsive-noise Modelmentioning

confidence: 99%

Impulsive noise filter using median- and partition-based operation

Chen

Zhao

2008

Opt. Eng

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A novel filter is proposed in this work to remove noise and preserve fine details from the impulse-corrupted images based on median and partition operation. Each pixel is classified into one of the mutually exclusive partition blocks by a novel observation vector. This observation vector can be directly used to partition the observation space without using predefined thresholds. The weight of each block is obtained using a learning approach based on the least-mean-square algorithm. The noise-filtering process is progressively applied through several iterations to achieve better noise-suppression results. Several experimental results have demonstrated that the proposed filter outperforms many filters in terms of both noise suppression and detail preservation.

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Section: Simulations On the Test Images 41 Impulsive-noise Modelmentioning

confidence: 99%

Impulsive noise filter using median- and partition-based operation

Chen

Zhao

2008

Opt. Eng

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“…For examples, to eliminate the impulsive noise from color images, a number of denoising algorithms have been exploited by first identifying the potential noise pixels in the color image and then employing a class of vector median filters only over those noise pixels that have been detected. Potential noise pixels can be detected either by classifying each pixel directly in RGB color space [1] or by setting some statistical rules based on the variation of local neighborhood [2,3]. But those noise detection approaches always need a training dataset or the presumable knowledge on constructing the statistical rules, which inevitably incur a significant misclassification error rate.…”

Section: Introductionmentioning

confidence: 99%

Statistical filtering of raster map images

Chen

Fränti

2010

2010 IEEE International Conference on Multimedia and Expo

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Filtering of raster map images or more general class of palette-indexed images can be considered as a discrete denoising problem with finite color output. Statistical features of local context are used to avoid damages of some specific but frequently occurring contexts caused by conventional filters. Several context-based approaches have been developed using either fixed context templates or context tree modeling. However, these algorithms are limited to deal with image with finite color input. In this paper, we further extended the method to a specific continuous-input-finite-output problem, in which the map images are corrupted by additive Gaussian noise instead. This extended method iteratively conducts a fusion procedure based on the probability distribution of pixels' intensity in RGB space and their conditional probabilities in the local contexts. Experimental results have demonstrated that the proposed algorithm is very efficient in filtering both impulsive and additive Gaussian noise.

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“…It is effective in impulsive noise filtering and great research efforts had been focused on morphological filters in the past two decades [2][3][4][5]. Morphological operation and structure element constitute the morphological filter.…”

Section: Introductionmentioning

confidence: 99%

A morphology filter to impulse noise based on adaptive optimizing the values of structure element

Wang

YiMin

2010

2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)

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This paper brings forward a new mathematical morphology method for impulse noise filtering. At first, it detects the impulse noises and selecting different morphological operators, and then adaptive optimizes the values of the structure element based on the pixels within the filtering window. Caring out formulations, the proposed method is lower in blurring the edge and higher in filtering effect.Keywords-impulse noise filtering; mathematical morphology; the values o/the structure element;signal to noise ratio (PSNR) I.

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An impulsive noise color image filter using learning-based color morphological operations

Cited by 25 publications

References 41 publications

Impulsive noise filter using median- and partition-based operation

Impulsive noise filter using median- and partition-based operation

Statistical filtering of raster map images

A morphology filter to impulse noise based on adaptive optimizing the values of structure element

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