周若飞,
邹昳琨
哈尔滨工业大学电子与信息工程学院 哈尔滨 150001
基金项目:国家自然科学基金(61671184, 61401120)，国家科技重大专项(2015ZX03001041)
详细信息
作者简介:王钢：男，1962年生，教授，博士生导师，主要研究方向为数据通信、物理层网络编码、通信网理论与技术
周若飞：男，1989年生，博士生，研究方向为压缩感知与图像处理、压缩感知与网络编码
邹昳琨：男，1992年生，博士生，研究方向为多无人机通信网络性能优化
通讯作者:王钢 gwang51@hit.edu.cn
中图分类号:TN911.73计量
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被引次数:0
出版历程
收稿日期:2019-09-02
修回日期:2019-11-19
网络出版日期:2019-11-28
刊出日期:2020-01-21
Research on Image Optimization Technology Based on Compressed Sensing
Gang WANG,,Ruofei ZHOU,
Yikun ZOU
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Funds:The National Natural Science Foundation of China (61671184, 61401120), The National Science and Technology Major Project (2015ZX03001041)
摘要
摘要:压缩感知(CS)理论是目前信息工程相关领域研究的前沿热点之一。它打破了传统的奈奎斯特采样定理，相比于其要求的最小采样频率，CS理论证明了能够从更低数目的采样中以高概率完整地恢复原始信号，在保证信息特征不丢失的前提下节省了数据采集和处理的时间成本。压缩感知理论本质上可以视为处理线性信号恢复问题的工具，因此在求解信号和图像的逆问题上有着显而易见的优势。图像退化问题便是其中之一，恢复相应的高质量图像的过程即为图像优化。为推动压缩感知理论的学术研究与实际应用，该文介绍了其基本原理与方法。根据图像优化技术的现存研究工作，分别从去噪、去模糊和超分辨三大主流方面研究了基于CS理论的优化技术。最后探讨了所面临的问题和挑战，分析了未来的发展趋势，为将来研究工作的展开提供借鉴与帮助。
关键词:图像处理/
压缩感知/
图像去噪/
图像去模糊/
超分辨
Abstract:Compressed Sensing (CS) theory is one of the most active research fields in electronic information engineering. CS theory overcomes the limits dictated by Nyquist sampling theorem. Compared to the required minimum sampling quantity, CS proves that the original signal can be restored with high probability by fewer measurements, which saves the time cost of data acquisition and processing without losing information features. CS theory can essentially be regarded as a tool for dealing with linear signal recovery problems, so it has obvious advantages in solving inverse problems of signals and images. Image degradation is one of them, and the process of restoring high-quality images is image optimization. In order to promote the academic research and practical application of CS theory, the basic principle of CS is introduced. Based on the previous research, this paper studies on CS-based image optimization technology in three main aspects: denoising, deblurring and super resolution. Finally, the problems and challenges are discussed, and the current trends are analyzed to provide reference and help for future work.
Key words:Image processing/
Compressed Sensing (CS)/
Image denoising/
Image deblurring/
Super resolution
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