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基于特征增强的轻量化可逆超分辨率网络

A Lightweight Reversible Super-Resolution Network Based on Feature Enhancement

  • 摘要: 针对当前深度超分网络参数量非常庞大, 且推理速度缓慢, 而轻量化网络在复杂环境条件下无法表达图像深层特征的问题, 提出基于特征增强的轻量化可逆超分辨率网络. 首先提出边缘特征残差, 配合所提的边缘相似损失指导模型重建, 增强重建图像对纹理轮廓的表达能力; 然后补充新的小波特征核, 使小波变换支持任意缩放因子的重建任务; 最后引入全局特征提取模块, 在特征图中嵌入自注意力机制, 提取全局特征. 在缩放因子为 4 时的基准测试集 Set5 上的实验结果表明, 与 SwinIR-light 相比, 所提网络表现更优, PSNR 提升 0.41 dB, 参数量减少 244k, 推理时间缩短 49.05%.

     

    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. 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%.

     

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