Filtering impulse noise in medical images using information sets

Adaptive Sequentially Weighted Median Filter for Image Highly Corrupted by Impulse Noise

“…By running Matlab R2019a on a machine with Intel(R) Core(TM) i7-7700 CPU at 3.60 GHZ, equipped with 8 GB RAM, we conduct the experiments on datasets SET12, BSD68, and medical images shown in FIGURE 6. The empirical validation for the proposed ASWMF is conducted by performing thorough comparative analyses with the stateof-the-art filters proposed recently in literatures, which are DAMF [11], NDBINF [13], PVGF [18], ADWMF [22], RBFI [24], SVMFF [25], NAISM [27], and INLM [30], in terms of noise detection accuracy, peak signal to noise ratio (PSNR), structural similarity index (SSIM) [33], edge preservation index (EPI) [34], image entropy H [35], visual perception, and computational time. The PSNR, SSIM, EPI, and H are defined by The stabilizing constant C 1 and C 2 are calculated with the dynamic range, L = 255, K 1 and K 2 , by default K 1 and K 2 are selected as 0.01 and 0.03, respectively [33].…”

“…The SVM classification based fuzzy filter (SVMFF) [25], aiming at performance improvement irrespective of noise density, employs a support vector machine classification for noise detection, along with a histogram based fuzzy filtering for noise removal. Inspired by the fuzzy switching median filters and the works on the concept of information sets, a noise adaptive information set based switching median filter (NAISM) is proposed in [27]; the information sets are derived from fuzzy sets to deal with the uncertainty, and by virtue of the switching criterion and the local effective information surrounding the noisy pixel, the best calculated value replaces the noisy pixel.…”

Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images

“…Chest X-ray images often get corrupted by the impulse (salt and pepper) noise [21] , [22] , [23] , [24] , [25] , [26] . This corruption is typically caused by a malfunctioning X-ray receiver, bit errors in X-ray image transmission, and faulty memory locations in hardware.…”

“…These gain factors eventually are used as wights for neighboring pixels that replace the noise-corrupted pixel. Using a fuzzy switching median filter and the concept of information sets, Arora et al introduced a filter to remove the impulse noise from images [25] . This method works in two phases: the first phase detects pixels corrupted by the impulse noise, and the second phase operates the filter on noisy pixels using an adaptive switching criterion.…”

Is large improvement in efficiency of impulsive noise removal in color images still possible?

“…For instance, in one-pixel attacks, altering only few pixels in an original image is enough to fool a deep neural network [6,7]. Therefore, filtering algorithms dedicated to the suppression of impulsive disturbances in color images and also considered as defensive methods against adversarial attacks, have attracted considerable interest among many researchers [8][9][10][11][12][13][14][15][16][17].…”