基于多尺度稠密残差网络的JPEG压缩伪迹去除方法

JPEG在高压缩比的情况下，解压缩后的图像会产生块效应、边缘振荡效应和模糊，严重影响了图像的视觉效果。为了去除JPEG压缩伪迹，该文提出了多尺度稠密残差网络。首先把扩张卷积引入到残差网络的稠密块中，利用不同的扩张因子，使其形成多尺度稠密块；然后采用4个多尺度稠密块将网络设计成包含2条支路的结构，其中后一条支路用于补充前一条支路没有提取到的特征；最后采用残差学习的方法来提高网络的性能。为了提高网络的通用性，采用具有不同压缩质量因子的联合训练方式对网络进行训练，针对不同压缩质量因子训练出一个通用模型。经实验表明，该文方法不仅具有较高的JPEG压缩伪迹去除性能，且具有较强的泛化能力。

JPEG Compression Artifacts Reduction Algorithm Based on Multi-scale Dense Residual Network

In the case of high compression rates, the JPEG decompressed image can produce blocking artifacts, ringing effects and blurring, which affect seriously the visual effect of the image. In order to remove JPEG compression artifacts, a multi-scale dense residual network is proposed. Firstly, the proposed network introduces the dilate convolution into a dense block and uses different dilation factors to form multi-scale dense blocks. Then, the proposed network uses four multi-scale dense blocks to design the network into a structure with two branches, and the latter branch is used to supplement the features that are not extracted by the previous branch. Finally, the proposed network uses residual learning to improve network performance. In order to improve the versatility of the network, the network is trained by a joint training method with different compression quality factors, and a general model is trained for different compression quality factors. Experiments demonstrate that the proposed algorithm not only has high JPEG compression artifacts reduction performance, but also has strong generalization ability.