Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

Mirza, Anwar M.; Chaudhry, Asmatullah; Munir, Badre

doi:10.1007/s11390-007-9061-3

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Performance analysis of the above-mentioned methods is carried out in terms of the average values of MSE, PSNR, SSIM, and wPSNR as shown in Table III. It can be observed that the performance of our proposed method is better as compared to PW (Pham and Klagner, 2000), AWF, and Adaptive Fuzzy Kriging (Mirza et al, 2007) in terms of all of the image quality measures.…”

Section: B Performance Comparison Across Standard Imagementioning

confidence: 89%

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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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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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Section: B Performance Comparison Across Standard Imagementioning

confidence: 89%

“…This article is an extension of our previous work (Mirza and Munir, 2004;Mirza et al, 2007) and aims at improving upon the existing fuzzy-kriging approach by proposing a new approach based on the concepts of fuzzy logic, punctual kriging, and genetic programming. This article makes the following contributions to image restoration:…”

Section: Introductionmentioning

confidence: 99%

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

Chaudhry

Khan

Ali

et al. 2007

Int J Imaging Syst Tech

Self Cite

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“…sensor noise) or transmission (i.e. channel noise) [1]. The median (MED) filter is commonly used for suppressing impulse noise due to its simple implementation and efficiency.…”

Section: Introductionmentioning

confidence: 99%

SVM-based Filter Using Evidence Theory and Neural Network for Image Denosing

Lin¹

2013

JSEA

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This paper presents a novel decision-based fuzzy filter based on support vector machines and Dempster-Shafer evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector that is used as the input data of the proposed SVM impulse detector. A fuzzy filtering mechanism, where the weights are constructed using a counter-propagation neural network, is employed. Experimental results shows that the proposed filter has better performance in terms of noise suppression and detail preservation

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“…human perception [12]. Images can become corrupted during any of the acquisition, preprocessing, compression, transmission, storage and/or reproduction phases of the processing [5].…”

mentioning

confidence: 99%

Fuzzy based Impulse Noise Reduction Method

Hussain

Bhatti

Jaffar

2011

Multimed Tools Appl

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In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-tonoise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.

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Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

Cited by 9 publications

References 10 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

SVM-based Filter Using Evidence Theory and Neural Network for Image Denosing

Fuzzy based Impulse Noise Reduction Method

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