Image Denoising Method Using Diffusion Equation and Edge Map Estimated with K-Means Clustering Algorithm

Abstract

The purpose of noise filtering is to preserve features such as edge or corners, and to reduce noise. However, it is difficult to preserve them after noise filtering, because most of noise filtering algorithms have the characteristic of low pass filter, which results in the loss of signal components with high frequencies like edge or corners. Noise filtering methods using diffusion equation try to solve this problem, and improve it to some extent. Edge detector has been used in filtering methods with diffusion equation to determine the speed of diffusion term, so the performance of them depends on the edge detection algorithm to some extent. Previous noise filtering approaches of diffusion equation use a simple gradient edge detector to determine the speed of diffusion. Also, each channel of color images are filtered separately, which result in the filtered image with distortion or blurring. In this paper, we propose a new noise filtering method for color images using diffusion equation and edge map estimated with K-means clustering algorithm together.

Local and Nonlocal Steering Kernel Weighted Total Variation Model for Image Denoising

“…Image denoising is a vital preprocessing step for image based object detection, recognition, and tracking [1][2][3][4][5][6]. Since high frequency image details are mixed with noise in most cases, most of the existing image denoising methods have difficult preserving the edge and texture information while thoroughly eliminating the noise [7][8][9][10][11][12].…”