At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP) framework. More specifically, with the assumption of Gaussian quantization noise, the SALR prior is utilized to effectively separate signal from noise and preserve image edges. Compared with previous low rank priors, the SALR reconstructs a better result via shape adaptive blocks. The QC prior is also adopted to avoid over-smoothing and to enable a more accurate estimation. Finally, by extracting features of external images, the mapping matrix of sparse dictionary pairs is trained to enhance image details. Extensive experimental results demonstrate that the proposed deblocking method has superior performances in both subjective vision and objective quality.

在低比特率下，通常在JPEG压缩图像中引入视觉上令人讨厌的块状伪像。在本文中，我们提出了一种结合形状自适应低秩（SALR）先验，量化约束（QC）先验和基于稀疏性的细节增强的图像去块方法。我们首先设计一个解块模型，以在最大后验（MAP）框架下获得初始的解块图像。更具体地，在假设高斯量化噪声的情况下，利用SALR先验有效地将信号与噪声分离并保留图像边缘。与先前的低秩先验相比，SALR通过形状自适应块重建了更好的结果。还采用了质量控制先验，以避免过度平滑并实现更准确的估计。最后，通过提取外部图像的特征，训练稀疏字典对的映射矩阵以增强图像细节。大量的实验结果表明，提出的解块方法在主观视觉和客观质量上均具有出色的性能。