Digital Image Enhancement and Noise Filtering by Use of Local Statistics

TLDR

The paper develops computational techniques for contrast enhancement and noise filtering of 2‑D image arrays using local mean and variance. The methods are nonrecursive, transform‑free, and process each pixel independently by estimating local mean and variance and applying a minimum‑mean‑square‑error filter, with a linear approximation for multiplicative noise. Experiments demonstrate that the algorithms provide effective filtering and are well suited for real‑time image processing applications.

Abstract

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.