l_(1)-αl_(2)最小化模型下不同噪声的误差估计  

The Error Estimation of Different Noises Under the Minimizing l_(1)-αl_(2)

作　　者：王俊丽 穆晓芳[2] 温瑞萍[1] WANG Junli;MU Xiaofang;WEN Ruiping(Department of Computer Science,Taiyuan Normal University,Shanxi Jinzhong 030619,China;School of Computer Science and Technology,Taiyuan Normal University,Shanxi Jinzhong 030619,China)

机构地区：[1]太原师范学院数学与统计学院,山西晋中030619 [2]太原师范学院计算机科学与技术学院,山西晋中030619

出　　处：《太原师范学院学报（自然科学版）》2023年第2期13-18,共6页Journal of Taiyuan Normal University:Natural Science Edition

摘　　要：压缩感知主要是考虑从较少的采样数据中以高概率精确地重构原高维稀疏信号.基于l_(1)-αl_(2)(0<α≤1)最小化模型,大多数文献研究信号的重构问题,而对于图像重构方面很少研究,尤其对于高斯噪声和l_(∞)-有界噪声下的图像重构.根据测量矩阵的约束等距性得到这两种噪声下图像重构的误差估计.Compressed sensing is mainly concerned with the accurate reconstruction of the original high-dimensional sparse signal with high probability from a small number of sampled data.Based on the l_(1)-αl_(2)(0<α≤1)minimization model,most literatures focus on signal reconstruction,but there are few researches on image reconstruction,especially for image reconstruction under Gaussian noise and l_(∞)-bounded noise.To study the error estimation of image reconstruction under these two kinds of noise based on the constrained isometry of measurement matrix.

关 键 词：压缩感知 图像重构 l_(1)-αl_(2)最小化 约束等距性 误差估计 

分 类 号：O211.5[理学—概率论与数理统计]