一种稀疏度拟合的图像自适应压缩感知算法

针对运用压缩感知理论对图像进行自适应压缩采样时，采样率及稀疏度阈值确定具有很强的主观性，提出一种稀疏度拟合的精确自适应采样算法.该算法通过循环迭代来确定各个稀疏度下满足PSNR要求的最低采样率，利用最小二乘法对稀疏度及采样率数据进行拟合，得到稀疏度-采样率选取的最佳目标函数.基于TVAL3重构算法对上述自适应采样算法进行了实验仿真，结果表明，重构图像的PSNR均高于用相同值的固定采样率重构的PSNR值，其中纹理特征区分明显的图像此PSNR差值能达到3.5 dB以上.相比粗糙自适应算法，平均采样率比其降低的同时，重构图像仍得到了更高的PSNR值. 

A Novel Algorithm on Adaptive Image Compressed Sensing with Sparsity Fitting

When the image is compressed adaptively with compressed sensing theory, the determination of sampling rate and sparsity threshold were highly subjective. In order to solve the problem, an accurately adaptive sampling algorithm with sparsity fitting was proposed in this paper. This algorithm determines the minimum sampling rate under certain sparseness to meet the PSNR requirements by iteration, and an optimal objective function of sparsity-sampling rate choices was obtained with the method of least squares fitting sparsity and sampling rate data. The adaptive sampling algorithm was simulated based on TVAL3. Experimental results show that the PSNR values of reconstructed images are higher than that with the same fixed sampling rate algorithm, and the PSNR difference of clear texture distinction images can reach more than 3.5 dB. Compared to the roughly adaptive algorithm, when the average sampling rate is lower than that, the reconstructed image obtains a higher PSNR value.