Cartoon-texture evolution for two-region image segmentation
针对现有模型对含噪声或纹理图像分割效果差的问题，提出基于卡通纹理分解的新模型，利用ADMM方法求解非光滑约束优化问题，实验证明其在平滑、噪声和纹理图像上的有效性。
Abstract Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. (SIAM J Appl Math 66(5):1632–1648, 2006) designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model.