A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise
Publication in refereed journal


摘要In this paper, a two-stage method for segmenting blurry images in the presence of Poisson or multiplicative Gamma noise is proposed. The method is inspired by a previous work on two-stage segmentation and the usage of an I-divergence term to handle the noise. The first stage of our method is to find a smooth solution u to a convex variant of the Mumford-Shah model where the l(2) data-fidelity term is replaced by an I-divergence term. A primal-dual algorithm is adopted to efficiently solve the minimization problem. We prove the convergence of the algorithm and the uniqueness of the solution u. Once u is obtained, in the second stage, the segmentation is done by thresholding u into different phases. The thresholds can be given by the users or can be obtained automatically by using any clustering method. In our method, we can obtain any K-phase segmentation (K >= 2) by choosing (K - 1) thresholds after u is found. Changing K or the thresholds does not require u to be recomputed. Experimental results show that our two-stage method performs better than many standard two-phase or multiphase segmentation methods for very general images, including antimass, tubular, magnetic resonance imaging, and low-light images.
著者Chan R, Yang HF, Zeng TY
期刊名稱SIAM Journal on Imaging Sciences
頁次98 - 127
關鍵詞convexity; Gamma noise; image segmentation; multiplicative noise; primal-dual algorithm; total variation
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Imaging Science & Photographic Technology; IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY; Mathematics; Mathematics, Applied; MATHEMATICS, APPLIED

上次更新時間 2020-22-10 於 01:41