Image Segmentation via Fischer-Burmeister Total Variation and Thresholding (Advances in Applied Mathematics and Mechanics, 2022)
Image segmentation is a significant problem in image processing. In thispaper, we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation (FBTV). The first stage of our method is to calculate a smoothsolution from the FBTV Mumford-Shah model. Furthermore, we design a new difference of convex algorithm (DCA) with the semi-proximal alternating direction methodof multipliers (sPADMM) iteration. In the second stage, we make use of the smooth solution and the K-means method to obtain the segmentation result. To simulate imagesmore accurately, a useful operator is introduced, which enables the proposed modelto segment not only the noisy or blurry images but the images with missing pixelswell. Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods, especially on the images withmissing pixels.