分块压缩感知的全变差正则化重构算法

In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT uses the minimization l₀ norm and total variation to construct the objective function. To solve the problem that l₀ norm term and the block measurement constraint can not be optimized directly, the iterative threshold method is used to minimize the l₀ norm of the reconstructed image, and the convex set projection is employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot" effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.