Abstract: 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. Aim-ing 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 re-construction 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 em-bed a self attention mechanism in the feature map to extract global features. The proposed network showed bet-ter performance than SwinIR-light on the benchmark test Set5 with a scaling factor of 4. It improved the PSNR by 0.41, reduced the number of parameters by 244K, and lowered the inference time by 49.05%.