Image restoration techniques generally use intrinsic correlations of image contents to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflecting connections between different image data, but such a difference is not taken into consideration in most existing schemes. Typically, sparse representation based works use the same image data to exploit both local and non-local correlation in shared regularization. This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. The proposed scheme adaptively selects different image data to exploit local and non-local correlation respectively. In particular, to exploit local correlation, the image data of interest is extracted from clustered rows of patch groups that consist of similar image contents. Experimental results on image denoising show that the proposed scheme not only outperforms state-of-the-art sparsity and low rank-based methods, but also surpasses recent successful deep learning-based approaches in terms of PSNR, SSIM, and visual quality.

中文翻译：

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CAS：图像去噪的相关自适应稀疏建模


图像恢复技术通常利用图像内容的内在相关性来减少未知信号的不确定性并估计潜在的地面真相。局部相关性和非局部相关性是所使用的两种主要相关性。它们是反映不同图像数据之间联系的不同相关性来源，但大多数现有方案并未考虑这种差异。通常，基于稀疏表示的作品使用相同的图像数据来利用共享正则化中的局部和非局部相关性。本文旨在分别充分利用图像内容的局部和非局部相关性，从而实现接近最优的稀疏表示，从而最小化信号的不确定性。所提出的方案自适应地选择不同的图像数据来分别利用局部和非局部相关性。特别是，为了利用局部相关性，从由相似图像内容组成的补丁组的聚集行中提取感兴趣的图像数据。图像去噪的实验结果表明，所提出的方案不仅优于最先进的稀疏性和基于低秩的方法，而且在 PSNR、SSIM 和视觉质量方面也超越了最近成功的基于深度学习的方法。