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Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Ashpreet, Mantosh Biswas
Copyright: © 2020 |Volume: 8 |Issue: 2 |Pages: 16
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781799808107|DOI: 10.4018/IJSI.2020040103
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MLA

Ashpreet, and Mantosh Biswas. "Impulse Noise Detection and Removal Method Based on Modified Weighted Median." IJSI vol.8, no.2 2020: pp.38-53. http://doi.org/10.4018/IJSI.2020040103

APA

Ashpreet & Biswas, M. (2020). Impulse Noise Detection and Removal Method Based on Modified Weighted Median. International Journal of Software Innovation (IJSI), 8(2), 38-53. http://doi.org/10.4018/IJSI.2020040103

Chicago

Ashpreet, and Mantosh Biswas. "Impulse Noise Detection and Removal Method Based on Modified Weighted Median," International Journal of Software Innovation (IJSI) 8, no.2: 38-53. http://doi.org/10.4018/IJSI.2020040103

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Abstract

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

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