Enhancement of hazy color images using a self-tunable transformation function

Abstract

Vision based outdoor mobile systems are very sensitive to infelicitous weather circumstances like hazy and foggy conditions. The acquisition of image frames in such an environment deteriorates the scene contrast and biases the color information. In order to recover the scene details, we propose a new method which takes a nonlinear approach, where the haze pixel intensity ismanipulated effectively with a specially designed sine nonlinear function. This function is integrated with the optics based haze model to approximate the enhanced inverse transmission of the scene. The transformation function is composed with a variable parameter,which tunes automatically, to produce desired nonlinear mapping for each pixel while maintaining the local contrast.Unlike other state-of art haze removal techniques, which operates on local regions, proposed method operates on each pixel to eliminate the blocking artifacts and minimizes the processing complexity. Our experimental results with quantitative measures demonstrate that the proposed technique yields state-of-the-art performance on hazy images and is suitable to process a dynamic video scenes captured in adverse weather conditions.