A Lightweight Reversible Super-Resolution Network Based on Feature Enhancement

Deep super-resolution reconstruction networks have a large number of parameters and slow inference speed. Lightweight networks unable to express deep features of images under complex environmental conditions. Aiming at those issues, a lightweight reversible super-resolution network based on feature enhancement is proposed. Firstly, edge feature residuals are proposed, combined with the proposed edge similarity loss to guide model reconstruction and enhance the expression of texture contours. Then, add a new wavelet feature kernel to support the reconstruction task with any scaling factors. Finally, a global feature extraction module is introduced to embed a self attention mechanism in the feature map to extract global features. The proposed network showed better performance than SwinIR-light on the benchmark test Set5 with a scaling factor of 4. It improved the PSNR by 0.41 dB, decreased the number of parameters by 244k, and reduced the inference time by 49.05%.