A Comparative Analysis of Impulse Noise Removal Techniques on Gray Scale Images
V. Murugan, T. Avudaiappan, R. Balasubramanian · International Journal of Signal Processing Image Processing and Pattern Recognition · 2014
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.