Dual-branch feature learning network for single image super-resolution

The feature extraction ability of some existing super-resolution networks is relatively weak. And these networks do not further process the extracted features. These problems make the networks often show limited performance, resulting in blurred details and unclear edges of the reconstructed images. Therefore, further research is needed to resolve these problems. In this paper, we propose a novel dual-branch feature learning super resolution network (DBSR). The core of DBSR is the dual-branch feature learning (DB) block. In order to enhance the ability of feature extraction, the block adopts a multi-level and dual-branch structure. At the same time, some components for further processing features are introduced in each branch to maximize the learning ability of the block. The reconstructed images of DBSR are clearer than other networks in line and contour, and better results are obtained in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For example, when the scaling factor is 2, the PSNR/SSIM on each test dataset is 38.25dB/0.9614, 34.11dB/0.9218, 32.35dB/0.9018, 32.94dB/0.9354 and 39.46dB/0.9783 respectively. The experimental results demonstrate that DBSR achieves better accuracy and visually pleasing than the current excellent methods.