EPSR: Edge Profile Super resolution

EPSR: Edge Profile Super resolution

Recently numerous deep convolutional neural networks(CNNs) have been explored in single image super-resolution(SISR) and they achieved significant performance. However, most deep CNN-based SR mainly focuses on designing wider or deeper architecture and it is hard to find methods that utilize image properties in SISR.In this paper, by developing an edge-profile approach based on end-to-end CNN model to SISR problem, we propose an edge profile super resolution(EPSR). Specifically, we construct a residual edge enhance block(REEB), which consists of residual efficient channel attention block(RECAB), edge profile(EP) module, and context network(CN) module. RE-CAB extracts adaptively rescale channel-wise features by considering interdependencies among channels efficiently.From the features, EP module generates edge-guided features by extracting edge profile itself, and then CN module enhances details by exploiting contextual information of the features. To utilize various information from low to high frequency components, we design a fractal skip connection(FSC) structure. Since self-similarity of the architecture, FSC structure allows our EPSR to bypass abundant information into each REEB block. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods.

EPSR：边缘轮廓超分辨率

近年来，已经在单图像超分辨率（SISR）中探索了许多深度卷积神经网络（CNN），并且它们取得了显着的性能。但是，大多数基于CNN的深度SR主要集中在设计更广泛或更深的体系结构上，因此很难找到在SISR中利用图像属性的方法。.. 在本文中，通过开发基于端到端CNN模型的边缘轮廓方法来解决SISR问题，我们提出了一种边缘轮廓超分辨率（EPSR）。具体来说，我们构造了一个残差边缘增强块（REEB），它由残差有效通道注意块（RECAB），边缘轮廓（EP）模块和上下文网络（CN）模块组成。RE-CAB通过有效地考虑通道之间的相互依赖性来提取自适应重新缩放通道方式的特征.EP模块从特征中提取边缘轮廓本身来生成边缘导向的特征，然后CN模块通过利用特征的上下文信息来增强细节。为了利用从低频到高频分量的各种信息，我们设计了分形跳过连接（FSC）结构。由于架构具有相似性，FSC结构使我们的EPSR可以将大量信息绕过每个REEB区块。实验结果表明，我们的EPSR与最先进的方法相比具有竞争优势。 （阅读更多）