Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Abstract

This study presents a novel noise detection algorithm which satisfactorily detects noisy pixels in images corrupted by random-valued impulse noise of high levels up to 80% noise density. Three levels of adaptive thresholds along with an auxiliary condition are used in this method which adequately addresses the drawbacks of existing methods, especially the miss detection of noise-free pixels as noisy pixels and vice versa. A noise signature is calculated for every pixel and compared with the first threshold to identify noise followed by the comparison of the central pixel with the second and third levels of thresholds. In addition to the standard deviation and mean, the concept of quartile has been used as another measure of dispersion. After detection, a fuzzy switching weighted median filter is applied to restore the corrupted image. The simulation results demonstrate that the proposed method is able to outperform the existing methods in both the detection and filtering of random-valued impulse noise in images.

The novel noise classification techniques found on quadruple threshold statistical detection filter under fix intensity impulse outlier environment

“…Next, the 2 nd experimental simulation of efficacy (in PSNR) of noise suppressing algorithm found on QTSD filter [28], which is correlatively analyzed with the classical TTSD filter [28], are illustrated in Table 4 under noise denseness from 5% to 90%. From these simulation results, the efficacy of noise suppressing algorithm found on QTSD filter and modified AMF [26]- [28] has equally efficacy with the classical TTSD for six noise photographs (lena, m-calendar, pepper, pentagon, house, airplane). However, classification correctness of the proposed QTSD has ultimately efficacy with the classical TTSD for other three noise photographs.…”

“…The modern noise classification techniques [25] such as ROAD, ROLD and RORD are comparatively analyzed for detecting impulse noise in 2019. Later, the noise suppressing algorithm found on the triple threshold statistical detection (TTSD) filter [26] in 2018 was introduced and the noise suppressing algorithm has very impressive effectiveness for random intensity impulse noise (RIIN). As a results, the TTSD has been analyzed its performance [27] for suppressing impulse noise for all density salt and pepper noise in 2019.…”

An innovative vigorous outlier recognition placed on LROAD for fix-amplitude impulsive noise

“…Many present outlier recognition approaches [25] placed on rank-ordered absolute differences (ROAD), rank-ordered logarithmic differences (ROLD) and rank-ordered relative differences (RORD) are relatively reexamines for recognizing impulse outlier in 2019. Next, the outlier suppression approach placed on the triple threshold statistical detection (TTSD) filter [26] was developed for RAIN in 2018. Subsequently, the outlier suppression approach placed on the localised rank ordered absolute differences (LROAD) filter [27], which is progressed from the ordinary ROAD approach, initially was developed for RAIN in 2016 and these approach provides superior effectiveness.…”

A Two-Stage Algorithm for the Detection and Removal of Random-Valued Impulse Noise Based on Local Similarity

“…Since three different thresholds are used in each detection stage to obtain a more robust filter, the running time of this method is longer than most other methods. Signh [24] uses three levels of adaptive thresholds and an auxiliary condition for detecting noise and restoring images which can improve the miss detection rate and false detection rate of existing noise detection algorithms, but it has better detection and denoising effects only in noise images with high density noise levels. Nadeem [25] proposed an image restoration technique based on adjacent pixels in the spatial link direction and fuzzy logic to solve medium and highly damaged grayscale images with random value impulse noise.…”