Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution. In this paper, we employ events to alleviate the burden of SRB and propose an Event-enhanced SRB (E-SRB) algorithm, which can generate a sequence of sharp and clear images with High Resolution (HR) from a single blurry image with Low Resolution (LR). To achieve this end, we formulate an event-enhanced degeneration model to consider the low spatial resolution, motion blurs, and event noises simultaneously. We then build an event-enhanced Sparse Learning Network (eSL-Net++) upon a dual sparse learning scheme where both events and intensity frames are modeled with sparse representations. Furthermore, we propose an event shuffle-and-merge scheme to extend the single-frame SRB to the sequence-frame SRB without any additional training process. Experimental results on synthetic and real-world datasets show that the proposed eSL-Net++ outperforms state-of-the-art methods by a large margin. Datasets, codes, and more results are available at https://github.com/ShinyWang33/eSL-Net-Plusplus.

翻译：从单张运动模糊图像进行超分辨率重建（SRB）是一个严重的不适定问题，因为同时存在运动模糊和低空间分辨率的联合退化。本文利用事件信息来减轻SRB的负担，提出了一种事件增强型SRB算法（E-SRB），该算法可以从单张低分辨率模糊图像生成一系列清晰的高分辨率图像。为实现这一目标，我们构建了一个事件增强的退化模型，同时考虑了低空间分辨率、运动模糊和事件噪声。在此基础上，我们基于双稀疏学习方案构建了事件增强稀疏学习网络（eSL-Net++），其中事件和强度帧均采用稀疏表示进行建模。此外，我们提出了一种事件混洗与合并方案，无需额外训练即可将单帧SRB扩展为序列帧SRB。在合成和真实世界数据集上的实验结果表明，所提出的eSL-Net++大幅优于现有最先进方法。数据集、代码及更多结果可在 https://github.com/ShinyWang33/eSL-Net-Plusplus 获取。