Fuzzy SVM based fuzzy adaptive filter for denoising impulse noise from color images

TLDR: FCM clustering has been incorporated with fuzzy- support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from color images and proposed FSVM based fuzzy adaptive filter provides better performance than some of the established state-of-art filters.

Abstract: Impulse noise is an “On-Off” noise that corrupts an image drastically. Classification of noisy and non-noisy pixels should be performed more accurately so as to restore the corrupted image with less blurring effect and more image details. In this paper, fuzzy c-means (FCM) clustering has been incorporated with fuzzy- support vector machine (FSVM) classifier for classification of noisy and non-noisy pixels in removal of impulse noise from color images. Here, feature vector comprises of newly introduced local binary pattern (LBP) with previously used feature vector prediction error, median value, absolute difference between median and pixel under operation. In this work, features have been extracted from the image corrupted with 10%, 50 and 90% impulse noise respectively and FCM clustering has been used for reduction of size of the feature vector set before processing through FSVM during training procedure. If the pixel is depicted as noisy in testing phase, fuzzy decision based adaptive vector median filtering is performed in accordance with available non-corrupted pixels within the processing window centring the noisy pixel under operation. It has been observed that proposed FSVM based fuzzy adaptive filter provides better performance than some of the established state-of-art filters in terms of PSNR, MSE, SSIM and FSIMC. It is seen that performance is increased by ~4 dB than baseline filters such as modified histogram fuzzy color filter (MHFC) and multiclass SVM based adaptive filter (MSVMAF).