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A learning-based method for compressive image recovery

机译：一种基于学习的压缩图像恢复方法

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Compressive sensing (CS) theory dictates that a sparse signal can be reconstructed from a few random measurements. An important issue of compressive image recovery (CIR) is that the optimal sparse space is usually unknown and/or it often varies spatially for non-stationary signals (e.g., natural images). In this paper, apart from fixed sparse spaces, prior models, specifically a set of piecewise autoregressive (AR) models that encode the common statistics of image micro-structures, are learned from example image patches, and they are then used to construct adaptive sparsity regularizers for CIR. Furthermore, a complementary non-local structural sparsity regularizer is also incorporated into the CIR process to improve the robustness. The regularization by local AR model and non-local redundancy makes the proposed CIR very effective. Experimental results on benchmark images validate that the proposed algorithm can outperform significantly previous CIR methods in terms of both PSNR and visual quality.
机译：压缩感测（CS）理论指示可以从一些随机测量结果中重建稀疏信号。压缩图像恢复（CIR）的一个重要问题是最优稀疏空间通常是未知的和/或对于非平稳信号（例如自然图像）它经常在空间上变化。在本文中，除了固定的稀疏空间外，还从示例图像块中学习了先验模型，特别是一组编码图像微结构的通用统计量的分段自回归（AR）模型，然后将其用于构建自适应稀疏性CIR的正则化器。此外，在CIR过程中还加入了补充性的非局部结构稀疏性正则化器，以提高鲁棒性。通过本地AR模型和非本地冗余进行的正则化使得所提出的CIR非常有效。在基准图像上的实验结果证明，该算法在PSNR和视觉质量方面都可以大大优于以前的CIR方法。

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