现实图像的显著性纹理结构可为分块压缩感知算法提供先验信息, 优化算法。鉴于此, 提出了一种新的基于显著性的自适应分块压缩感知算法。算法所提显著性是以灰度空间相关矩阵和韦伯定律为基础, 采用确定性正交对称托普利兹矩阵对目标图像进行测量, 提出了均熵最小化自适应分块策略、角二阶矩最大化块向量生成方式以及合成特征依据下的自适应采样率设置, 并结合不同重构算法进行了分析和验证。实验表明, 所提算法策略在多项指标上较传统算法具有更好的表现, 易于硬件实现, 针对不同重构算法和测试图像具有普适性和稳定性。

The salient texture structure of actual images can provide priori information for Block Compressed Sensing (BCS) algorithm and optimize the algorithm. Based on this, a new Adaptive Block Compressed Sensing (ABCS) algorithm based on saliency is proposed. The saliency in the proposed algorithm is built on the theory of gray-level spatial-dependence matrix and Weber's theorem. The deterministic Orthogonal Symmetric Toeplitz Matrix (OSTM) is adopted to measure the target image. The adaptive block strategy minimizing the average entropy, the block-vector generation method maximizing the angle second-order moment and the adaptive sampling rate setting under the synthetic feature are proposed. The analysis and verification are carried out by using different basic reconstruction algorithms. Experiment results show that, compared with the traditional algorithm, the proposed algorithm performs better on different indexes, is easy to implement by hardware and has universality and stability for different reconstruction algorithms and test images.