论文标题
EPSR：Edge Crofile超级分辨率
EPSR: Edge Profile Super resolution
论文作者
论文摘要
在本文中，我们提出了Edge Crofile Super分辨率（EPSR）方法，以保留结构信息并恢复纹理。我们通过堆叠修饰的分形残差网络（MFRN）结构来层次，反复地堆叠EPSR。 MFRN由许多由三个不同模块组成的残留边缘轮廓块（REPB）组成，例如剩余有效的通道注意块（RECAB）模块，边缘配置文件（EP）模块和上下文网络（CN）模块。重述具有高频组件的更有用的功能。从该功能中，EP模块通过生成边缘配置文件本身产生结构的知情功能。最后，CN模块通过利用高频信息（例如纹理和结构）捕获细节。当重复MFRN结构中的过程时，我们的EPSR可以提取高保真特征，因此可以防止纹理损失并保持适当的清晰度结构。实验结果表明，我们的EPSR针对PSNR和SSIM评估指标的最新方法以及视觉结果实现了竞争性能。
In this paper, we propose Edge Profile Super Resolution(EPSR) method to preserve structure information and to restore texture. We make EPSR by stacking modified Fractal Residual Network(mFRN) structures hierarchically and repeatedly. mFRN is made up of lots of Residual Edge Profile Blocks(REPBs) consisting of three different modules such as Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. RECAB produces more informative features with high frequency components. From the feature, EP module produce structure informed features by generating edge profile itself. Finally, CN module captures details by exploiting high frequency information such as texture and structure with proper sharpness. As repeating the procedure in mFRN structure, our EPSR could extract high-fidelity features and thus it prevents texture loss and preserves structure with appropriate sharpness. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods in PSNR and SSIM evaluation metrics as well as visual results.
