基于迭代-帧间双预测的CUP-VISAR重构方法OA北大核心

CUP-VISAR system is a new technology that combines Compressed Ultrafast Photography(CUP)with two-dimensional Velocity Interferometer System for Any Reflector(VISAR).To solve the problem that the image reconstruction quality of CUP-VISAR system decreases obviously under the condition of large noise,a compressed ultrafast photography reconstruction method based on iteration-interframe dual prediction is proposed.Using this method,the correlation of inter-frame image data and the correlation of iterations before and after the same frame image are studied.The compressed image reconstruction problem is presented as an iteration-inter frame dual prediction optimization problem based on Kalman prediction and inter-frame prediction,and the Plug-and-Play Generalized Alternating Projection(PnP-GAP)framework is used to solve the optimization problem effectively.Simulation results show that the minimum Peak Signal-to-Noise Ratio(PSNR)and minimum Structure Similarity Index Measure(SSIM)of the proposed method are increased by 3.18-2.11 dB and 20.30%-8.22%under large Gaussian noise conditions.The practical results show that the proposed method can obtain higher definition of fringe image,and the reconstructed line-VISAR(1D-VISAR)fringe movement trend is clearer,which verifies the effectiveness of the algorithm.