针对图像边缘与轮廓不能精确重构的问题,提出了一种基于灰度共生矩阵的多尺度分块压缩感知算法。该算法利用三级离散小波变换将图像分解为高频部分和低频部分。通过灰度共生矩阵的熵分析高频部分图像块的纹理复杂度,并根据图像块纹理进行再分块、自适应分配采样率。采用平滑投影Landweber算法重构图像,消除分块引起的块效应。对多种图像进行压缩重构仿真,实验结果表明,无观测噪声情况、采样率为0.1时,本算法在Mandrill图像上得到的峰值信噪比(PSNR)为25.37 dB,比现有非均匀分块算法提高了2.51 dB。不同噪声水平下,本算法的PSNR比无噪时仅下降了0.41~2.05 dB。对于纹理复杂度较高的图像,本算法的重构效果明显优于非均匀分块算法,对噪声具有较好的鲁棒性。

Aiming at the problem that image edges and contours cannot be accurately reconstructed, a multi-scale block-based compressed sensing algorithm based on gray-level co-occurrence matrix is proposed in this paper. The algorithm uses three-level discrete wavelet transform to decompose the image into high-frequency part and low-frequency part. The entropy of the gray-level co-occurrence matrix is used to analyze the texture complexity of the high-frequency part of the image block, and the image block texture is subdivided and the sampling rate is adaptively allocated. The smooth projection Landweber algorithm is utilized to reconstruct the image and eliminate the blocking effect caused by the block. Compression and reconstruction simulation of various images are conducted. Experimental results show that when there is no observation noise and the sampling rate is 0.1, the peak signal-to-noise ratio (PSNR) obtained by the algorithm on Mandrill images is 25.37 dB, which is 2.51 dB higher than the existing non-uniform block algorithm. Under different noise levels, the PSNR of the algorithm is only 0.41-2.05 dB lower than that of no noise. For the image with high texture complexity, the reconstruction effect of the algorithm is obviously better than that of non-uniform block algorithm, and has good robustness to noise.