Image dehazing using total variation regularization

Images of outdoor scenes are often degraded by particles and water droplets in the atmosphere. Haze, fog, and smoke are such phenomena due to atmospheric absorption and scattering. Numerous image dehazing (haze removal) methods have been proposed in the last two decades, and the majority of them employ an image enhancing or restoration approach. Different variants of local adaptive algorithms for single image dehazing are also known. A haze-free image must have higher contrast compared with the input hazy image. It is possible to remove haze by maximizing the local contrast of the restored image. Some haze removal approaches estimate a dehazed image from an observed hazed scene by solving an objective function, whose parameters are adapted to local statistics of the hazed image inside a moving window. In the signal and image processing a common way to solve the denoising problem utilizes the total variation regularization. In this presentation we propose a new algorithm combining local estimates of depth maps toward a global map by regularization the total variation for piecewise-constant functions. Computer simulation results are provided to illustrate the performance of the proposed algorithm for restoration of hazed images.