基于变分模型的块压缩感知重构算法

为了提高现有块压缩感知重构算法的性能，提出了基于全变分和混合变分模型的块压缩感知（简称BCS-TV和BCS-MV）算法。该方法以块为单位进行图像采样，以自然图像正则项的稀疏性为先验条件，通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM)，在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比，该算法的PSNR约提高1.5 dB，SSIM约提高0.05，运行速度较稳定，特别适合具有固定传输时延的多媒体数据处理场合。

Reconstruction algorithm for block compressed sensing based on variation model

The algorithms for block compressed sensing based on total variation and mixed variation (abbreviated as BCS-TV and BCS-MV) models were proposed to improve the performance of current reconstruction algorithms for the block-based com-pressed sensing. In the measuring phase, an image was sampled block-by-block. In the recovering period, it took the sparse regularization of the natural image as a priori knowledge, and approached the target function within the whole image through the modified augmented Lagrange method and alternating direction method of multipliers (ALM-ADMM). The method proposed achieves average PSNR gain of 1.5 dB and SSIM gain of 0.05 at a more stable running speed, over the previous uniformly block-based compressed sensing. It is particularly suitable for the applications of the multimedia data processing with fixed transmission delay.