Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging With Convergence Analysis  

作　　者：Baoshun Shi Kexun Liu 

机构地区：[1]School of Information Science and Engineering,Yanshan University [2]the Hebei Key Laboratory of Information Transmission and Signal Processing,Qinhuangdao 066004,China [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第11期2136-2153,共18页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (62371414,61901406);the Hebei Natural Science Foundation (F2020203025);the Young Talent Program of Universities and Colleges in Hebei Province (BJ2021044);the Hebei Key Laboratory Project (202250701010046);the Central Government Guides Local Science and Technology Development Fund Projects(216Z1602G)。

摘　　要：Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.

关 键 词：Bounded denoiser compressed sensing magnetic resonance imaging(CSMRI) dual frames plug-and-play priors REGULARIZATION 

分 类 号：R445.2[医药卫生—影像医学与核医学]