现有的自适应多尺度分块压缩感知算法忽略了高频信息在重建中的作用,导致图像的边缘轮廓得不到充分重建;并且在压缩分块过程中采用固定分块大小,没有充分利用图像自身的稀疏性。针对上述不足,提出一种多尺度分块的自适应采样率压缩感知算法。该算法充分利用小波变换后的高频信号和低频信号,同时针对图像的固定尺寸分块进行改进。首先,对低频部分利用自适应邻域特征的空域滤波算法消除块效应;其次,对高频部分依据纹理特征自适应选取图像块的大小,实现样本块尺寸的自动划分和采样率的自适应;最后,分别对纹理信息各异的图像进行压缩重建仿真。结果表明,本方法重建效果明显优于已有的自适应采样率算法。

In the existing adaptive multi-scale block-slice compression sensing algorithms, the role of high-frequency information in the reconstruction process is neglected, resulting in the not-complete-reconstruction of the edge contours of images. Moreover, the fixed block size is used in the process of compressing blocks, and thus the sparsity of the image itself is not fully used. In view of the above deficiencies, a multi-scale block adaptive sampling rate compression sensing algorithm is proposed. This algorithm makes full use of the high-frequency and low-frequency signals after wavelet transform, and simultaneously improves the fixed size block of images. First, the spatial filtering algorithm based on adaptive neighborhood features is used to eliminate the blockness in the low frequency part. Second, as for the high frequency part, the size of the image block is adaptively selected according to the texture features, and thus the sample block size is automatically partitioned and the sampling rate is adaptive. Finally, the images with different amounts of texture information are compressed and reconstructed. The results show that the reconstruction effect by the proposed method is obviously superior to those by the existing adaptive sampling rate algorithms.