A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

Rashmi Kumari (JJTU, India), Anupriya Asthana (Galgotias University, India) and Vikas Kumar (Asia-Pacific Institute of Management, India)
Copyright: © 2017 |Pages: 17
DOI: 10.4018/978-1-5225-1908-9.ch006
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Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.
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Degradation of images usually occurs due to addition of noise during image acquisition and transmission. Each element in the imaging chain such as lenses, film, digitizer, etc. is responsible for introducing noises and causing degradation. Hence, some noise reduction technique is essential to suppress the noise by retaining important features of the images according to different applications. Noise occurs in the images when the pixels are randomly failed and replaced by other values in an image. The image model containing impulse noise can be described as:

where, Sij represents the noiseless image pixel and Nij represents the noise substituting for the original pixel. The fixed and random-valued impulse noise have been discussed in a variety of impulse noise models for images. Fixed-valued impulse noise, known as the “salt-and-pepper” noise, is made up of corrupted pixels whose values are replaced with values equal to the maximum or minimum (255 or 0) of the allowable range with equal probability (p/2). The random-valued impulse noise is made up of corrupted pixels, the values are replaced by random values, uniformly distributed in the range [0, 255]. Several modifications have been applied on conventional filtering techniques in order to remove noise as nonlinear filtering techniques tend to blur fine details and destroy edges while removing corrupted pixels. (Lee, Kuo & Yu, 1997);(Lee & Kuo, 2000);(Xu, Zhu & Wang, 2004); (Sheng & Runato, 2000); (Boo, Ibrahim & Toh, 2009).

In recent years, research is being carried out in the field of digital image processing with conjunction of soft computing technique. Fusion of Adaptive Neural Network (ANN) and Fuzzy Inference System (FIS) is frequently applied by researchers in various scientific and engineering research areas to tackle real world issues. When the classic techniques are not sufficient to produce an easy and accurate solution, neural networks and fuzzy logic are applied together to a variety of problems in digital image processing (Yuksel & Bas, 2003); (Yuksel & Bas, 2005); (Yuksel & Yildirim, 2004). As both paradigms have their own advantages, it becomes imperative to combine their features and make a strong tool to solve complex problems.

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