  JPEG is one of the most commonly used standards among lossy image compression
methods. However, JPEG compression inevitably introduces various kinds of
artifacts, especially at high compression rates, which could greatly affect the
Quality of Experience (QoE). Recently, convolutional neural network (CNN) based
methods have shown excellent performance for removing the JPEG artifacts. Lots
of efforts have been made to deepen the CNNs and extract deeper features, while
relatively few works pay attention to the receptive field of the network. In
this paper, we illustrate that the quality of output images can be
significantly improved by enlarging the receptive fields in many cases. One
step further, we propose a Dual-domain Multi-scale CNN (DMCNN) to take full
advantage of redundancies on both the pixel and DCT domains. Experiments show
that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.
