TY - JOUR
T1 - Image Segmentation via Fischer-Burmeister Total Variation and Thresholding
JO - Advances in Applied Mathematics and Mechanics
VL - 4
SP - 960
EP - 988
PY - 2022
DA - 2022/04
SN - 14
DO - http://doi.org/10.4208/aamm.OA-2021-0126
UR - https://global-sci.org/intro/article_detail/aamm/20442.html
KW - Image segmentation, Fischer-Burmeister total variation, difference of convex algorithm, sPADMM, K-means method.
AB - <p style="text-align: justify;">Image segmentation is a significant problem in image processing. In this
paper, 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 smooth
solution from the FBTV Mumford-Shah model. Furthermore, we design a new difference of convex algorithm (DCA) with the semi-proximal alternating direction method
of 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 images
more accurately, a useful operator is introduced, which enables the proposed model
to segment not only the noisy or blurry images but the images with missing pixels
well. Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods, especially on the images with
missing pixels.</p>