An Adaptive Regularized Fast Iterative Shrinkage-Thresholding Algorithm for Image Reconstruction in Compressed Sensing
Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing,this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm.This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function.During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value.Simulation results show that the proposed algorithm,compared with the traditional algorithms,obtains the better reconstructed image quality and lower time complexity.
compressed sensing image reconstruction fast iterative shrinkage-thresholding adaptive regularization
Xin Meng Shifang Duan Shexiang Ma
College of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China
国际会议
太原
英文
775-779
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）