基于掩膜预处理的稀疏表示和压缩感知图像重建

近年来兴起的压缩感知（compressive sensing, or compressed sampling，CS）理论对信号稀疏性的要求，使信号的稀疏表示得到了前所未有的关注。考虑到现实信号往往是非稀疏性的，而压缩感知理论要求被测信号必须满足稀疏性或在某个规范正交基下满足稀疏性，因此信号的稀疏性表示变得十分重要。主要研究探索了二值掩膜预处理的稀疏表示方法。结合二值掩膜的算法去除人眼不敏感的DCT系数，在不影响图像主观质量的前提下提高测量系数的稀疏度。实验表明提出的预处理方法减少了CS的重建时间，并且提高了图像的重建质量。

The reconstruction of compressive sensing and sparse representation based mask pretreatment

In recent years, the rise of the theory of compressed sensing signal sparsity requirements, so sparse representation of the signal received unprecedented attention. Taking into account the real signal is often non sparsity, and compressive sensing theory must meet the requirements of the measured signal sparsity or meet at a certain sparseness orthonormal base, so sparse representation of the signal becomes very important. This paper studies explored the sparse representation of the binary mask pretreatment. The algorithm combines the binary mask the human eye is not sensitive to the removal of the DCT coefficients to improve the measurement sparsity factor in subjective image without compromising quality. Experimental results show that our proposed method reduces pretreatment CS reconstruction time and improve the quality of the reconstructed image.