A reflectance re-weighted Retinex model for non-uniform and low-light image enhancement

Abstract

Image enhancement is a fundamental low-level task of significant importance that can directly affect high-level image processing tasks. Although various methods have been proposed to enhance images, the effectiveness of current methods deteriorates significantly under non-uniform lighting. Since the brightness may vary dramatically in different regions of real-world photos, current methods hardly achieve a good balance between enhancing low-light regions and retaining normal-light regions in the same image. Consequently, either the low-light regions are under-enhanced or the normal-light regions are over-enhanced, while at the same time, color distortion and artifacts are frequently found. To overcome this shortcoming, we propose a robust Retinex-based model with reflectance map re-weighting that can improve the brightness level of the low-light image and re-balance the brightness concurrently. We introduce an alternating scheme to solve our proposed model, in which the illumination map, reflectance map, and weighting map are updated iteratively. By utilizing the regularization terms, the noise is well-suppressed during the process. An initialization scheme for the weighting map is also proposed to make our model adaptable to a wide range of light conditions. To the best of our knowledge, we are the first to propose a variational model with an explicitly constructed re-weighting prior and the associated weighing map concept for the reflectance map. It can estimate the reflectance map, suppress noise, and re-balance the brightness simultaneously. A series of experimental results on a variety of popular datasets demonstrate the efficacy of our method and its superiority in enhancing real low-light images when compared to other state-of-the-art methods.