利用信息熵的岩心图像自适应压缩感知重构

Adaptive Compressed Sensing Reconstruction of Core Images Using Information Entropy

针对BCS-SPL算法对岩心图像进行压缩感知重构的细节模糊的问题,提出一种利用信息熵的岩心图像BCS-SPL压缩感知重构算法.采用小波变换对岩心图像进行稀疏表示,对各子带进行多尺度分块,依据信息熵的大小自适应分配采样率并确定观测矩阵,通过维纳滤波结合Landweber迭代操作实现重构.实验结果表明,在相同采样率下,与原始的BCS-SPL算法相比,该算法的重构质量提高了2-4 d B.

Aimed at the details vague problem of Block Compressed Sensing-Smooth Projected Landweber compressed sensing reconstruction of core images, a Block Compressed Sensing-Smooth Projected Landweber compressed sensing reconstruction of core images using information entropy is proposed. The method introduces discrete wavelet transform into the sparse representation and conducts multiscale block for each subband, and then adaptively allocates the sampling rates and determines the measurement matrix. The reconstruction can be achieved by Wiener filter combined with Landweber iterative. The experimental results show that the reconstruction quality is improved by 2-4d B compared with that of Block Compressed Sensing-Smooth Projected Landweber under the same sampling rates.