Fuzzy entropy based impulse noise detection and correction method for digital images

Fuzzy entropy based impulse noise detection and correction method for digital images

Автор: S.Vijaya Kumar, C.Nagaraju

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 3 vol.10, 2018 года.

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Impulse noise is the prime factor which reduces the quality of the digital image and it erases the important details of the images. De-noising is an indispensable task to restore the image features from the corrupted low- quality images and improve the perceptual quality of images. Several techniques are used for image quality enhancement and image restoration. In this work, an image de-noising scheme is developed to detect and correct the impulse noise from the image by using fuzzy entropy. The proposed algorithm is designed in two phases, such as noise detection phase, and correction phase. In the noise detection phase, the fuzzy entropy of pixels in a window of interest (WoI) is computed to detect whether the pixel is noisy or not. The Fuzzy entropy of pixel greater than specified alpha cut value will be considered as noise pixel and submitted to correction phase. In the correction phase noise pixel value is replaced with a fuzzy weighted mean of the un-corrupted pixels in the WoI. The proposed Fuzzy entropy based impulse noise detection and correction method are implemented using MATLAB. The experimentation has been carried out on different standard images and the analysis is performed by comparing the performance of the proposed scheme with that of the existing methods such as DBA, MDBUTMF, AMF, NAFSM, BDND, and CM , using PSNR, SSIM, and NAE as metric parameters. The proposed method will give good results compared to state of the art methods in image restoration.

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Fuzzy entropy, a window of interest, impulse noise, image restoration

Короткий адрес: https://sciup.org/15015947

IDR: 15015947   |   DOI: 10.5815/ijigsp.2018.03.05

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