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Head office: 2 nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad www.kresttechnology.com , E-Mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434

Fuzzy Random Impulse Noise Removal

From Color Image Sequence

Synopsis

INTRODUCTION

IMAGES and videos belong to the most important information carriers in todays world (e.g.,

traffic observations, surveillance systems, autonomous navigation, etc.). However, the images

are likely to be corrupted by noise due to bad acquisition, transmission or recording. Such

degradation negatively influences the performance of many image processing techniques and a

preprocessing module to filter the images is often required. In this paper, we have presented a

new filtering framework for color videos corrupted with random valued impulse noise. In order

to preserve the details as much as possible, the noise is removed step by step. The detection of

noisy color components is based on fuzzy rules in which information from spatial and temporal

neighbors as well as from the other color bands is used. Detected noisy components are filtered

based on blockmatching where a noise adaptive mean absolute difference is used and where thesearch region contains pixels blocks from both the previous and current frame.

Abstract in this paper, a new fuzzy filter for the removal of random impulse noise in color

video is presented. By working with different successive filtering steps, a very good tradeoff

between detail preservation and noise removal is obtained. One strong filtering step that should

remove all noise at once would inevitably also remove a considerable amount of detail.

Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic

variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged.

Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute

difference. The experiments show that the proposed method outperforms other state-of-the-art

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Head office: 2 nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad www.kresttechnology.com , E-Mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434

filters both visually and in terms of objective quality measures such as the mean absolute error

(MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD). Index

Terms Circuits and systems, computers and information processing, computational and

artificial intelligence, filtering, filters, fuzzy logic, image denoising, logic, nonlinear filters.

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