Deep Artifact-Free Residual Network for Single Image Super-Resolution
This addresses image quality issues for applications like medical imaging or photography, but it is incremental as it builds on existing residual learning approaches.
The paper tackles the problem of artifacts in single-image super-resolution by proposing a Deep Artifact-Free Residual (DAFR) network that uses residual learning and ground-truth images as targets, achieving better quantitative and qualitative image quality compared to existing methods.
Recently, convolutional neural networks have shown promising performance for single-image super-resolution. In this paper, we propose Deep Artifact-Free Residual (DAFR) network which uses the merits of both residual learning and usage of ground-truth image as target. Our framework uses a deep model to extract the high-frequency information which is necessary for high-quality image reconstruction. We use a skip-connection to feed the low-resolution image to the network before the image reconstruction. In this way, we are able to use the ground-truth images as target and avoid misleading the network due to artifacts in difference image. In order to extract clean high-frequency information, we train the network in two steps. The first step is a traditional residual learning which uses the difference image as target. Then, the trained parameters of this step are transferred to the main training in the second step. Our experimental results show that the proposed method achieves better quantitative and qualitative image quality compared to the existing methods.