Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images. Single-Image SR generally handles each image independently, but ignores the temporal information implied in continuing frames. Multi-frame SR is able to model the temporal dependency via capturing motion information. However, it relies on neighbouring frames which are not always available in the real world. Meanwhile, slight camera shake easily causes heavy motion blur on long-distance-shot low-resolution images. To address these problems, a Blind Motion Deblurring Super-Reslution Networks, BMDSRNet, is proposed to learn dynamic spatio-temporal information from single static motion-blurred images. Motion-blurred images are the accumulation over time during the exposure of cameras, while the proposed BMDSRNet learns the reverse process and uses three-streams to learn Bidirectional spatio-temporal information based on well designed reconstruction loss functions to recover clean high-resolution images. Extensive experiments demonstrate that the proposed BMDSRNet outperforms recent state-of-the-art methods, and has the ability to simultaneously deal with image deblurring and SR.

中文翻译：

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盲动去模糊超分辨率：当动态时空学习遇到静态图像理解

单图像超分辨率 (SR) 和多帧 SR 是超分辨率低分辨率图像的两种方法。Single-Image SR 通常独立处理每个图像，但忽略连续帧中隐含的时间信息。多帧 SR 能够通过捕获运动信息对时间依赖性进行建模。然而，它依赖于现实世界中并不总是可用的相邻帧。同时，在远距离拍摄的低分辨率图像上，轻微的相机抖动很容易导致严重的运动模糊。为了解决这些问题，提出了一种盲运动去模糊超分辨率网络 BMDSRNet，用于从单个静态运动模糊图像中学习动态时空信息。运动模糊图像是相机曝光过程中随时间推移的积累，而所提出的 BMDSRNet 学习反向过程并使用三流学习基于精心设计的重建损失函数的双向时空信息，以恢复干净的高分辨率图像。大量实验表明，所提出的 BMDSRNet 优于最近最先进的方法，并且能够同时处理图像去模糊和 SR。