压缩传感
稀疏逼近
计算机科学
图像(数学)
秩(图论)
人工智能
先验概率
代表(政治)
奈奎斯特-香农抽样定理
过程(计算)
模式识别(心理学)
算法
计算机视觉
数学
贝叶斯概率
组合数学
操作系统
政治
法学
政治学
作者
Zhiyuan Zha,Bihan Wen,Xin Yuan,Saiprasad Ravishankar,Jiantao Zhou,Ce Zhu
出处
期刊:IEEE Signal Processing Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:40 (1): 32-44
被引量:9
标识
DOI:10.1109/msp.2022.3217936
摘要
The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process. Beyond exploiting local image properties, advanced CS schemes adopt nonlocal image modeling by extracting similar or highly correlated patches at different locations of an image to form a group to process jointly. More recent learning-based CS schemes apply nonlocal structured sparsity priors using group sparse (and related) representation (GSR) and/or low-rank (LR) modeling, which have demonstrated promising performance in various computational imaging and image processing applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI