Combining Total Variation and Nonlocal Variational Models for Low-Light Image Enhancement

Abstract

[eng] Images captured under low-light conditions impose significant limitations on the performance of computer vision applications. Therefore, improving their quality by discounting the effects of the illumination is crucial. In this paper, we present a low-light image enhancement method based on the Retinex theory. Our approach estimates illumination and reflectance in two steps. First, the illumination is obtained as the minimizer of an energy functional involving total variation regularization, which favours piecewise smooth solutions. Next, the reflectance component is computed as the minimizer of an energy functional involving contrast-invariant nonlocal regularization and a fidelity term preserving the largest gradients of the input image.