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Cluster Computing

Erschienen in:

25.01.2018

Super-resolution compressed sensing imaging algorithm based on sub-pixel shift

verfasst von: Bing Xu, Xiaoping Zhang, Xianjun Wu

Erschienen in: Cluster Computing | Sonderheft 4/2019

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Abstract

At present, some digital signal processing methods have attracted more and more attention in improving the resolution of images. Sub-pixel shift has been widely applied in improving the resolution of compressed sensing imaging system. The resolution of the compressed sensing imaging system is limited by pixel size of the modulation system. To overcome the resolution limitation of compressed sensing imaging system, a sub-pixel shift method is proposed to enhance the resolution of modulation information and achieve super-resolution images by compressed sensing imaging system. The principle of the proposed method is introduced and the proposed method is verified using numerical simulations. Experimental results revealed that the proposed method can effectively improve the resolution of compressed sensing imaging system and obtain super-resolution image information. Additionally, the signal to noise ratio of restoration results is positively related to the sample size.

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Titel: Super-resolution compressed sensing imaging algorithm based on sub-pixel shift
verfasst von: Bing Xu
Xiaoping Zhang
Xianjun Wu
Publikationsdatum: 25.01.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1839-2

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