Dr. Wenguang Liu Profile

Dr. Wenguang Liu

at CIOMP CAS

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Proceedings Article | 12 March 2021 Paper

Analysis and correction of the image motion for the rolling shutter CMOS detector

Yun-Hui Li, Xiao-Dong Wang, Wen-Guang Liu, Peng-Ji Zhou, Jing-Tao Huang

Proceedings Volume 11763, 117630C (2021) https://doi.org/10.1117/12.2585685

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The exposure mechanism of the rolling shutter CMOS detector is elaborated, which has the characteristics of nonsimultaneous exposure of each line of pixels. On this basis, the effects of image motion on imaging are analyzed, including image blur and image distortion, and thus obtaining the limit range of exposure time setting. According to the working mechanism of the rolling shutter, the forward coordinate transformation of image distortion and its distortion results are clarified. Then the distortion correction method and theoretical model are deduced in reverse, including two steps of coordinate transformation and resampling. Simulation results show that the method can effectively correct distortion. Finally, the contrast of the corrected images is compared when the rolling direction and the image motion are in different directions. The results show that when the rolling direction is perpendicular to the image motion, the highest average contrast can be obtained, but with the maximum uncertainty. When the rolling direction is in the same direction as the image motion, the average contrast is the lowest, but with the minimum uncertainty. Considering comprehensively the complexity of the image distortion correction algorithm and the average contrast from the image output, it is an optimal choice to set the CMOS detector's rolling direction perpendicular to the image motion.

SPIE Journal Paper | 3 March 2020

Self-adaptive block-based compressed sensing imaging for remote sensing applications

Xiaodong Wang, Yun-Hui Li, Zhi Wang, Wenguang Liu, Dan Liu, Jianing Wang

JARS, Vol. 14, Issue 01, 016513, (March 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.016513

KEYWORDS: Image restoration, Image processing, Compressed sensing, Imaging systems, Remote sensing, Image compression, Image quality, Detection and tracking algorithms, Data storage, Signal processing

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In order to effectively alleviate the pressure of high-resolution imaging and massive data storage and transmission, it is of great practical significance to introduce compressed sensing into remote sensing applications. From the perspective of imaging control strategy, the typical block-based compressed sensing (BCS) system is optimized. Based on the fact that there are generally significant differences between regions of remote sensing images, a self-adaptive BCS method is proposed. Compared with the traditional BCS system, the prior information of the imaging target is obtained first by adding a presampling process. On the one hand, it is used to generate a saliency information map, which guides the reasonable allocation of self-adaptive sampling ratios between blocks in the compressed sampling process, thereby improving the sampling efficiency. On the other hand, it is used to generate the weighted sparse coefficient matrix, which will be substituted into the theoretical model in the image restoration process, thus improving the image restoration efficiency. The experimental results show that the imaging quality of the proposed method has a significant improvement compared with the traditional system and is also superior to several existing self-adaptive methods. In addition, on the basis of the above method, a multiangle image restoration strategy is proposed, which further improves the image quality at the cost of four times the image restoration time.

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