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.
Frame difference method is a good method for motion segmentation, but its result contains much wrong motion regions and incomplete motion objects. In this paper we combine variational method with frame difference method to propose two motion segmentation models, and the proposed models are based on different invariance assumptions. The models can detect motion objects and make up for the inadequacy of frame differential method with smooth terms. Experimental results show that the proposed models can detect motion objects better.