Efficient L1-based nonlocal total variational model of Retinex for image restoration

Abstract

Characteristics of an image, such as smoothness, edge, and texture, can be better preserved using the nonlocal differential operator in image processing. We establish an L1-based nonlocal total variational (NLTVL1) model based on Retinex theory that can be solved by a fast computational algorithm via the alternating direction method of multipliers. Experiential results demonstrate that our NLTVL1 method has a good performance on enhancing contrast, eliminating the influence of nonuniform illumination, and suppressing noise. Furthermore, compared with previous works, including traditional Retinex methods and variational Retinex methods, our proposed approach achieves superior performance on edge and texture preservation and needs fewer iterations on recovering the reflectance image, which is illustrated by examples and statistics.