Feedback Multi-scale Residual Dense Network for image super-resolution

Abstract

The image super-resolution algorithm based on deep learning has a good reconstruction effect, and the reconstruction can be further enhanced by using multi-scale features. There are different extraction methods for multi-scale features, and current deep learning-based super-resolution algorithms often use only one method when utilizing multi-scale features. We use an error feedback mechanism with a dense residual mechanism to fuse multi-scale features and propose Feedback Multi-scale Residual Dense Network (FMDN), which uses two different multi-scale features to enhance the reconstruction effect. On the other hand, in the previous multi-scale feature fusion often used the method of concatenating. We design a new error feedback-based feature fusion method, and the experimental results show that it has better results than the common method of concatenating. In addition, we further use the feedback mechanism of recurrent to improve the efficiency of the module, which can use fewer layers to achieve the effect of more layers of the basic model, and take up less space, faster, or make a network with a larger number of layers have better results. Compared with the state-of-the-art method, the proposed method shows promising performance according to qualitative and quantitative evaluation.