Non-Convex and Convex Coupling Image Segmentation via TGpV Regularization and Thresholding

被引:9
作者
Wu, Tingting [1 ]
Shao, Jinbo [1 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Sci, Nanjing 210023, Jiangsu, Peoples R China
关键词
Two-stage strategy; non-convex and convex coupling; total generalized p-variation (TGpV); alternating direction method of multipliers (ADMM); clustering methods; TOTAL GENERALIZED VARIATION; MUMFORD-SHAH MODEL; ACTIVE CONTOURS; APPROXIMATION; SUPERRESOLUTION; MINIMIZATION; ENERGY; GRAPH;
D O I
10.4208/aamm.OA-2019-0199
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we propose a non-convex and convex coupling variational model for image segmentation. We design the non-convex and convex regularization terms based on total generalized p-variation (TGpV) regularizer to preserve the boundary of segmented parts and detect the structure in the image. Our method has two stages. The first stage is to approximate the Mumford-Shah model. The second stage is to segment the smoothed u into different phases by using a thresholding strategy. We develop a scheme based on the alternating direction method of multipliers (ADMM) algorithm, generalized p-shrinkage operation and K-means clustering method to carry out our method. We perform numerical experiments on many kinds of images such as real Bacteria image, Tubular magnetic resonance angiography (MRA) image, magnetic resonance (MR) images, anti-mass images, artificial images, noisy or blurred images. Some comparisons are arranged to show the effectiveness and advantages of our method.
引用
收藏
页码:849 / 878
页数:30
相关论文
共 50 条
[21]   A non-convex low-rank image decomposition model via unsupervised network [J].
Shang, Wanqing ;
Liu, Guojun ;
Wang, Yazhen ;
Wang, Jianjun ;
Ma, Yuemei .
SIGNAL PROCESSING, 2024, 223
[22]   Non-convex sparse regularisation [J].
Grasmair, Markus .
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 2010, 365 (01) :19-28
[23]   Simultaneous Image Enhancement and Restoration with Non-convex Total Variation [J].
Kang, Myeongmin ;
Jung, Miyoun .
JOURNAL OF SCIENTIFIC COMPUTING, 2021, 87 (03)
[24]   A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization [J].
Simoes, Miguel ;
Bioucas-Dias, Jose ;
Almeida, Luis B. ;
Chanussot, Jocelyn .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (06) :3373-3388
[25]   Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage [J].
Lin, Fan ;
Chen, Yingpin ;
Chen, Yuqun ;
Yu, Fei .
ALGORITHMS, 2019, 12 (10)
[26]   Non-convex variational model for image restoration under impulse noise [J].
Liu, Xinwu .
SIGNAL IMAGE AND VIDEO PROCESSING, 2022, 16 (06) :1549-1557
[27]   Non-convex variational model for image restoration under impulse noise [J].
Xinwu Liu .
Signal, Image and Video Processing, 2022, 16 :1549-1557
[28]   POLYNOMIALS ASSOCIATED TO NON-CONVEX BODIES [J].
Levenberg, N. ;
Wielonsky, F. .
ACTA MATHEMATICA HUNGARICA, 2021, 165 (02) :415-449
[29]   Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization [J].
Chen, Ruijuan ;
Tang, Xiaoquan ;
Li, Xiuting .
FRACTAL AND FRACTIONAL, 2022, 6 (12)
[30]   FAST FEASIBILITY PURSUIT FOR NON-CONVEX QCQPS VIA FIRST-ORDER METHODS [J].
Konar, Aritra ;
Sidiropoulos, Nicholas D. .
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, :4064-4068