LEARNING MULTI-SCALE FEATURES FOR JPEG IMAGE ARTIFACTS REMOVAL

A recently proposed quantization-table convolutional network (QCN) has proven as a state-of-the-art JPEG image artifacts removal method. To reduce computational complexity, the QCN learns image features from the down-sampled version of the input image. Consequently, the performance might be compromised as some salient features that can only be learned from the original input image with full resolution will be lost. To solve this problem, a novel multi-scale feature extraction block (MFEB) is proposed in this paper, which contains a coarse-scale branch and a fine-scale branch for learning salient image features from the down-sampled input image and the original full resolutions, respectively. To avoid introducing too much additional computational complexity due to the fine-scale branch, in our MFEB, this branch uses only one layer, while the coarse-scale branch exploits multiple layers. The feature sets obtained from the two branches are then fused. With the MFEB, a novel multi-scale artifacts removal network (MARN) is then developed to remove JPEG image artifacts. Extensive experiments have clearly shown that our MARN can deliver superior performance to that of a number of state-of-the-art methods.