Image restoration by adaptive fuzzy optimal filter

We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity-estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy-based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration.

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A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

Chaudhry

Khan

Ali

et al. 2007

Int J Imaging Syst Tech

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A new fuzzy logic filter for image enhancement

Farbiz

Menhaj

Motamedi

et al. 2000

IEEE Trans. Syst., Man, Cybern. B

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This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

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Histogram-based fuzzy filter for image restoration

Wang

Liu²,

Lin³

2002

IEEE Trans. Syst., Man, Cybern. B

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In this paper, we present a novel approach to the restoration of noise-corrupted image, which is particularly effective at removing highly impulsive noise while preserving image details. This is accomplished through a fuzzy smoothing filter constructed from a set of fuzzy membership functions for which the initial parameters are derived in accordance with input histogram. A principle of conservation in histogram potential is incorporated with input statistics to adjust the initial parameters so as to minimize the discrepancy between a reference intensity and the output of defuzzification process. Similar to median filters (MF), the proposed filter has the benefits that it is simple and it assumes no a priori knowledge of specific input image, yet it shows superior performance over conventional filters (including MF) for the full range of impulsive noise probability. Unlike in many neuro-fuzzy or fuzzy-neuro filters where random strategy is employed to choose initial membership functions for subsequent lengthy training, the proposed filter can achieve satisfactory performance without any training.

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Image restoration by adaptive fuzzy optimal filter

Cited by 3 publications

References 3 publications

A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

A new fuzzy logic filter for image enhancement

Histogram-based fuzzy filter for image restoration

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