Research on Image Optimization Technology Based on Compressed Sensing

Gang WANG; Ruofei ZHOU; Yikun ZOU

doi:10.11999/JEIT190669

Volume 42 Issue 1

Jan. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(1): 222-233

Gang WANG, Ruofei ZHOU, Yikun ZOU. Research on Image Optimization Technology Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669

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Gang WANG, Ruofei ZHOU, Yikun ZOU. Research on Image Optimization Technology Based on Compressed Sensing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 222-233. doi: 10.11999/JEIT190669

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Research on Image Optimization Technology Based on Compressed Sensing

doi: 10.11999/JEIT190669

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)

Received Date: 2019-09-02
Rev Recd Date: 2019-11-19

Available Online: 2019-11-28

Publish Date: 2020-01-21

Abstract

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.
- Image processing,
- Compressed Sensing (CS),
- Image denoising,
- Image deblurring,
- Super resolution

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References(83)

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