Mu Jing, Xiong Ruiqin, Fan Xiaopeng, Liu Dong, Wu Feng, Gao Wen
IEEE Trans Image Process. 2020 Mar 3. doi: 10.1109/TIP.2020.2975931.
Block transform coded images usually suffer from annoying artifacts at low bit-rates, because of the independent quantization of DCT coefficients. Image prior models play an important role in compressed image reconstruction. Natural image patches in a small neighborhood of the high-dimensional image space usually exhibit an underlying sub-manifold structure. To model the distribution of signal, we extract sub-manifold structure as prior knowledge. We utilize graph Laplacian regularization to characterize the sub-manifold structure at patch level. And similar patches are exploited as samples to estimate distribution of a particular patch. Instead of using Euclidean distance as similarity metric, we propose to use graph-domain distance to measure the patch similarity. Then we perform low-rank regularization on the similar-patch group, and incorporate a non-convex lp penalty to surrogate matrix rank. Finally, an alternatively minimizing strategy is employed to solve the non-convex problem. Experimental results show that our proposed method is capable of achieving more accurate reconstruction than the state-of-the-art methods in both objective and perceptual qualities.
由于离散余弦变换（DCT）系数的独立量化，块变换编码图像在低比特率时通常会出现恼人的伪影。图像先验模型在压缩图像重建中起着重要作用。高维图像空间中一个小邻域内的自然图像块通常呈现出潜在的子流形结构。为了对信号分布进行建模，我们提取子流形结构作为先验知识。我们利用图拉普拉斯正则化在块级别表征子流形结构。并将相似块用作样本以估计特定块的分布。我们不是使用欧几里得距离作为相似性度量，而是提出使用图域距离来衡量块相似性。然后我们对相似块组进行低秩正则化，并引入非凸lp罚项来替代矩阵秩。最后，采用交替最小化策略来解决非凸问题。实验结果表明，我们提出的方法在客观和感知质量方面都能够比现有方法实现更准确的重建。
