Open AccessDe-noising of an Image using Fuzzy Inference System and Performance Comparison with the Conventional system
Author(s) -
Ahmed S. Farhan,
Rezwan us Saleheen,
Chen Li Wei,
Farhan Mahbub
Publication year - 2021
Publication title -
journal of engineering advancements
Language(s) - English
Resource type - Journals
eISSN - 2708-6437
pISSN - 2708-6429
DOI - 10.38032/jea.2021.03.007
Subject(s) - salt and pepper noise , mean squared error , pixel , noise (video) , fuzzy logic , median filter , peak signal to noise ratio , noise reduction , computer science , artificial intelligence , filter (signal processing) , matlab , fuzzy inference , mathematics , adaptive neuro fuzzy inference system , image (mathematics) , computer vision , fuzzy control system , statistics , image processing , operating system
Noise prevailing in the image can diminish the physical appearance of the objects existing within the image and make them frail. Present research emphasizes a fuzzy inference system eradicating several types of noise from the images. The investigation implies the utilization of different levels of Salt & Pepper noise. Followed by the pixel determination applying a mask, the disparity between the focused pixel's intensity with the minimum, average, and maximum power of the chosen window has been determined. Since two fuzzy valued outputs have been obtained to match them, the one provided by a low noise rate would demonstrate the more accurate filter for the selected window. Utilizing Matlab the Peak Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE) are determined for evaluating the noise reduction performance. However, these values of PSNR and MSE obtained from this research are also compared with the conventional fuzzy filtering system.