A Comparative Analysis of Impulse Noise Removal Techniques on Gray Scale Images

Abstract

Various kinds of images and pictures are required as sources of information for analysis and interpretation. When an image is converted from one form to another such as scanning, transmitting, digitizing, storing etc., degradation occurs to the output image. Hence, the output image needs to be enhanced in order to be better analyzed. Denoising is the one of the pre processing technique in digital image processing. This paper investigates the performance of four denoising methods for removing the High Density Impulse Noise. They are Adaptive Bilateral Filter (ABF), Fuzzy Peer Group Filter (FPGF), Switching Bilateral Filter (SBF), and Boundary Discriminative Noise Detection Filter (BDND).The performance of the above four filters is compared by using five performance metrics. They are Peak-Signal-to-Noise-Ratio, Mean Square Error and Root Mean Square Error. The Experimental results show that the BDND filter based denoising method performs well than the other three methods.

Various Types of Image Noise and De-noising Algorithm

“…In communication channel testing and modeling, Gaussian noise is used as additive white noise to generate additive white Gaussian noise. The likelihood circulation capacity of Gaussian noise is ringer formed [5]. Communication channels can be affected by wideband Gaussian noise coming from many natural sources, such as the thermal vibrations of atoms in conductors, shot noise, black body radiation from the earth and other warm objects, and from celestial sources such as the Sun.…”

“…Therefore, every single pixel of an image influenced i.e. an image influenced by Gaussian commotion is zero [5].…”

“…The purposes for the event of this noise are the sharp and sudden changes of image flag, by memory cell disappointment, by breaking down of cameras sensor cells, by synchronization mistakes in image digitizing or transmission [6]. [5] Clean molecule in image securing source or overheated defective part can likewise precipitate this sort of commotion. In this sort of commotion, the image is ruined because of high contrast spots on it, having dull pixels in splendid districts and brilliant pixels in dim locales.…”

“…At commonsense grounds the photon clamor and other sensor based commotion degenerate the flag at various extents the presence of this noise is seen because of the factual way of electromagnetic waves, for example, x-beams, obvious lights and gamma beams. The x-beam and gamma beam sources discharged number of photons per unit time [5]. These beams are infused in patient's body from its source, in medicinal x beams and gamma beams imaging frameworks.…”

“…In this channel, we don't supplant the pixel estimation of image with the mean of all neighboring pixel values, we replaces it with the middle esteem. At that point the middle is ascertained by first sorting all the pixel values into rising request and afterward supplants the pixel being computed with the center pixel [5]. On the off chance that the neighboring pixel of image which is to be considered contains an even quantities of pixels, than the normal of the two center pixel qualities is utilized to supplant.…”