Abstract：To solve the problem that image compressive sensing reconstruction algorithm via nonlocal lowrank regularization could not adequately exploit the local gradient sparsity, an improved image construction algorithm was proposed based on lowrank and total variation regularization. The similar patches were found by image block matching method and formed into nonlocal similar patch groups. The regularization term of combining lowrank prior of nonlocal similarity patch groups with gradient was embedded into reconstruction model, which was solved by alternating direction multiplier method(ADMM) to obtain the reconstructed image. The test images were gray scale images. To verify the proposed algorithm, the experimental results were compared by subjective vision and peak signaltonoise ratio(PSNR). The experimental results show that compared with the algorithm via nonlocal lowrank, the proposed method can significantly improve the quality of reconstructed image with nonlocal selfsimilar structure precisely described, and the PSNR of reconstructed images is increased about 1 dB in average.
杨桄, 封磊, 孙怀江, 孙权森. 基于低秩和全变差正则化的图像压缩感知重构[J]. 江苏大学学报（自然科学版）, 2017, 38(5): 571-575.
YANG Guang, FENG Lei, SUN Huai-Jiang, SUN Quan-Sen. Image compressive sensing reconstruction based on lowrank and total variation regularization[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2017, 38(5): 571-575.
2017, Vol. 38 
