Local complexity difference matting based on weight map and alpha mattes

Image matting is an essential image processing technology in computer vision with significant diverse practical applications, including image synthesis, webcasting, and movie production. Although some methods have been proposed to extract the alpha mattes, these methods are not sensitive enough to local complex regions with large differences between the input image and the ground-truth alpha matte. In this paper, we design a weight map generation algorithm, which extracts the local complex region of each image by measuring the local complex differences between the input image and the ground-truth alpha matte. For the weight consistency problem of the pixel-level loss function, we propose a loss function based on local complexity differences, which can strengthen the training on local regions of large complexity differences. Moreover, we design a local complexity difference matting approach on the basis of the presented loss function and weight map generation algorithm to improve the matting accuracy of local complexity difference images. To verify the validity of the proposed matting method, experiments were conducted on the composition-1 k matting evaluation data set produced by Adobe. Experimental results show that the proposed weight map generation algorithm can effectively extract the local complex regions. Our proposed matting method outperforms state-of-the-art matting methods in the cases of locally complexity difference images.