Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.

中文翻译：

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提升从隐式多图像先验中学到的单图像超分辨率

基于学习的单图像超分辨率（SISR）旨在学习从低分辨率（LR）图像到高分辨率（HR）版本的多功能映射。关键的挑战是使网络训练偏向连续而尖锐的边缘。在这项工作中，我们首次提出了从多视图观测中学到的隐式边界，以显着减轻我们概述的SISR的挑战。具体地，对视差信息和场景的边界结构进行编码的多图像先验监督用于边缘保留的SISR网络。为简单起见，在我们框架的训练过程中，光场（LF）作为有效的多图像先验，并且混合损失函数共同考虑4D LF数据的内容，结构，方差和视差信息。因此，为了进行推断，这样的通用训练方案可以提高各种SISR网络的性能，特别是对于沿边缘的区域。在具有代表性的骨干SISR架构上进行的大量实验不断地证明了所提出方法的有效性，在不修改网络架构的情况下获得了约0.6 dB的增益。