25 October 2016 Laser interference effect evaluation method based on character of laser-spot and image feature
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Proceedings Volume 10158, Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage; 1015804 (2016) https://doi.org/10.1117/12.2243794
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
Evaluating the laser interference effect to CCD objectively and accurately has great research value. Starting from the change of the image’s feature before and after interference, meanwhile, considering the influence of the laser-spot distribution character on the masking degree of the image feature information, a laser interference effect evaluation method based on character of laser-spot and image feature was proposed. It reflected the laser-spot distribution character using the distance between the center of the laser-spot and center of the target. It reflected the change of the global image feature using the changes of image’s sparse coefficient matrix, which was obtained by the SSIM-inspired orthogonal matching pursuit (OMP) sparse coding algorithm. What’s more, the assessment method reflected the change of the local image feature using the changes of the image’s edge sharpness, which could be obtained by the change of the image’s gradient magnitude. Taken together, the laser interference effect can be evaluated accurately. In terms of the laser interference experiment results, the proposed method shows good rationality and feasibility under the disturbing condition of different laser powers, and it can also overcome the inaccuracy caused by the change of the laser-spot position, realizing the evaluation of the laser interference effect objectively and accurately.
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Jianfeng Tang, Jianfeng Tang, Xiaolin Luo, Xiaolin Luo, Lingxia Wu, Lingxia Wu, } "Laser interference effect evaluation method based on character of laser-spot and image feature ", Proc. SPIE 10158, Optical Communication, Optical Fiber Sensors, and Optical Memories for Big Data Storage, 1015804 (25 October 2016); doi: 10.1117/12.2243794; https://doi.org/10.1117/12.2243794
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