Abstract
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.
| Translated title of the contribution | Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 2217-2223 |
| Number of pages | 7 |
| Journal | Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology |
| Volume | 41 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 1 Sept 2019 |