Impulse Noise Filtering: Review of the State-of-the-Art Algorithms for Impulse Noise Filtering

Impulse Noise Filtering: Review of the State-of-the-Art Algorithms for Impulse Noise Filtering

Abhijit Chandra (Jadavpur University, India) and Srideep Maity (Indian Institute of Technology, Kharagpur, India)
DOI: 10.4018/978-1-4666-8654-0.ch005
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Digital images are often corrupted by various types of noises amongst which impulse noise is most prevalent. Impulse noise appears during transmission and/or acquisition of images. Intrusion of impulse noise degrades the quality of the image and causes the loss of fine image details. Reducing the effect of impulse noise from corrupted images is therefore considered as an essential task to be performed before letting the image for further processing. However, the process of noise reduction from an image should also take proper care towards the preservation of edges and fine details of an image. A number of efficient noise reduction algorithms have already been proposed in the literature over the last few decades which have nurtured this issue with utmost importance. Design and development of new two dimensional (2D) filters has grown sufficient interest amongst the researchers. This chapter attempts to throw enough light on the advancement in this field by illustratively describing existing state-of-the-art filtering techniques along with their capability of denoising impulse noises.
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2. Variants Of Impulse Noise

Images are often corrupted by impulse noise during the process of acquisition and transmission. Impulse noise may be of type unipolar or bipolar. Bipolar impulse noise is commonly known as salt-and-pepper noise (SPN) (Gonzalez & Woods, 2002; Huang, Yang, & Tang, 1979). The main property of salt-and-pepper noise is that the pixel corrupted by this noise gets the maximum or minimum value present in the dynamic range of available values, such as 0 and 255 in case of 8-bit gray-scale image. The SPN is commonly modeled in accordance with

(1) where Xi,j and Yi,j denote the intensity value of the original and corrupted images at coordinate (i,j) respectively and p is the noise density.

Key Terms in this Chapter

Local Features: The features of an image which have a limited spatial extent i.e. cover only a small portion of the image such as contours, probable presence of edges etc.

Genetic Programming: Genetic programming is a model of programming influenced by the concepts of biological evolution.

Image Denoising: Image denoising is the process of removing noise from an image.

Neural Network: Neural network is a computational model built in accordance with the central nervous system of human being.

Fuzzy Logic: Fuzzy logic is a problem solving tool of artificial intelligence which deals with approximate reasoning rather than fixed and exact reasoning.

Sharpness: Sharpness is an important image quality factor. It determines the amount of detail an image can convey.

Image Restoration: Image restoration is the process of recovering original pixel information from degraded or noisy image.

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