Perception-oriented Single Image Super-Resolution Network with Receptive Field Block

In recent years, deep learning has been widely applied to single image super-resolution(SISR). However, the majority of deep learning methods employ the Mean Square Error(MSE) loss as the objective optimization function, and the generated results are frequently too smooth and lack of details. In addition, the high-frequency information of the reconstructed image is severely lost, resulting in a generated image with poor visual effects. In order to address the aforementioned issues, this paper proposes a super-resolution network (RRFDB-GAN) with a receptive field module with a generative adversarial network as the main framework. The network adopts the receptive field block (RFB), which enables it to extract the features in multiple scales and to improve the discriminability. In this paper, the Residual in Residual Dense Block (RRDB) and the Residual of Receptive Field Dense Block (RRFDB) are combined into a new module, called Basic block. This module enhances the capability of feature reconstruction for low-resolution images. During the upsampling stage, a combination of Nearest Neighborhood Interpolation and Sub-pixel convolution is used to reduce the computational complexity and provide additional contextual information for super-resolution reconstruction, while achieving satisfactory performance. Finally, the four Basic blocks are integrated with the upsampling module into a simple end-to-end framework. Extensive experimental results demonstrate that the proposed method in this paper shows more details on the five test sets and outperforms other methods in terms of quantitative metrics and perception assessment.