Refined Filtering of Image Noise Using Local Statistics
Jong-Sen Lee · 1980
An effective algorithm for digital image noise filtering is presented in this paper. Mostnoise filtering techniques such as Kalman filter and transform domain methods require extensive image modeling and produce filtered images with considerable contrast loss. The algorithm proposed in this paper is an extension of Lee's local statistics method modified to utilize local gradient information. It does not require image modeling, and it will not smear edges and subtle details. For both the additive and multiplicative noise cases, the local mean and variance are computed from a reduced set of pixels depending on the orientation of the edge. Consequently, noise along the edge is removed, and the sharpness of the edge is enhanced. For practical applications when the noise variance is spatially varying and unknown, an adaptive filtering algorithm is developed. Experiments show its good potential for processing real-life images. Examples on images containing 256×256 pixels are given to substantiate the theoretical development.