Suppression of Random Valued Impulse Noise in Image Processing: A Review

Abstract

Nowadays a good low complexity denoising technique is necessary as pre-processing operation in many real-time practical applications. Images get corrupted with impulse noise due to the process of image transmission and image acquisition. In the process of impulse noise filtering it is necessary to preserve edges and details of the image. Also to avoid image smoothing, only corrupted pixel must be filtered. Comprehensive survey of various denoising techniques has been focused in this paper. This paper illustrates the survey of different low complexity methods such as Median, Adaptive Center Weighted Median (ACWM), Adaptive Median Filter (AMF) and high complexity methods such as Alpha-trimmed Mean Based Method (ATMBM), Differential Rank Impulse Detector (DRID) and Rank Ordered Relative Difference (RORD).The most effective technique to remove random valued impulse noise without losing useful information with pleasing denoised image is by decision-tree based impulse detector and direction oriented edge preserving image filter. This design requires low computational cost, few memory buffers, no iterations and most suited to be applied to many real-time applications. Also this design can be efficiently designed with FPGA.KEYWORDS: corrupted pixel, denoising, direction oriented edge preserving image filter, impulse detector, random valued impulse noise. I.INTRODUCTIONDuring the process of image digitization, transmission and also due to malfunctioning pixel elements in the sensors of the camera, incorrect memory locations, and incorrect timing in analog-to-digital conversion, images are often corrupted by impulse noise. An important characteristic of this type of noise is that only parts of the pixels are corrupted and the rest are noise free. There are many applications in image processing such as face recognition, edge detection, medical imaging, scanning, printing, license plate detection where it is important to remove noise in the images before these subsequent processes. Noise in the image affects the subsequent process. Thus various techniques for removing impulse noise in images are described in this paper.Impulse noise is categorized into two methods based on distribution of the pixel values. The noise which has either minimum or maximum pixel value in grey scale image is called fixed valued impulse noise. It is also known as salt and pepper noise. The noise in which pixel values are uniformly distributed in the rang [0 255] in grey scale image is known as random valued impulse noise. Removal of salt and pepper noise in image is easy as compared with random valued impulse noise. There are most of the techniques which are reported till now works very well for salt and pepper noise but fails under random valued impulse noise. It is also observed that detection mechanism decides the performance of the filtering scheme. Thus better detector gives the good performance of filtering scheme. So performance of the detector is very important. The performance of the detector is depend on the threshold value whi...

Kalman Filter With Impulse Noised Outliers : a Robustsequential Algorithm to Filter Data With a Large Number Of outliers

“…Different algorithms allow an image reconstruction under impulsive noise and most of them [33] use spatial similarity (correlation) of adjacent pixels like the median filter. Here, we consider longitudinal data and assume similarly time-correlations through a Gaussian Markov model (X k ) k≥1 .…”