Image enhancement using local intensity distribution equalization

Abstract

This paper proposes a local intensity distribution equalization (LIDE) method for image enhancement. LIDE applies the idea of histogram equalization to parametric model in order to enhance an image using local information. It reduces the amount of computational resources required by traditional method like the adaptive histogram equalization, but allows enhancing detail similar to the latter technique. Integral image was used to efficiently estimate local statistics needed by the parametric model. This data structure drastically reduces the computational cost especially for megapixel image where a large local window is preferred. It should be noted that, with a large local window, the intensity distribution could contain multiple peaks. LIDE can nicely handle such complex distribution via mixture of parametric models. To speed-up the mixture parameter estimation, we propose an EM algorithm that is also based on the integral image data structure. Experimental results show that LIDE produces an enhanced image with greater detail and lower noise compared to several existing methods.