Image deblurring is a classic problem in low-level computer vision with the
aim to recover a sharp image from a blurred input image. Advances in deep
learning have led to significant progress in solving this problem, and a large
number of deblurring networks have been proposed. This paper presents a
comprehensive and timely survey of recently published deep-learning based image
deblurring approaches, aiming to serve the community as a useful literature
review. We start by discussing common causes of image blur, introduce benchmark
datasets and performance metrics, and summarize different problem formulations.
Next, we present a taxonomy of methods using convolutional neural networks
(CNN) based on architecture, loss function, and application, offering a
detailed review and comparison. In addition, we discuss some domain-specific
deblurring applications including face images, text, and stereo image pairs. We
conclude by discussing key challenges and future research directions.

该论文综述了基于深度学习的图像去模糊方法，包括问题的起源、数据集、性能度量、方法分类、应用及挑战与未来方向。

深度图像去模糊：综述

Deep Image Deblurring: A Survey

This survey article summarizes research trends on the topic of anomaly
detection in video feeds of a single scene. We discuss the various problem
formulations, publicly available datasets and evaluation criteria. We
categorize and situate past research into an intuitive taxonomy and provide a
comprehensive comparison of the accuracy of many algorithms on standard test
sets. Finally, we also provide best practices and suggest some possible
directions for future research.

本论文总结了单一场景视频流异常检测的研究动向，包括问题表述、公开数据集、评估标准、算法比较和最佳实践，并提出未来研究的可能方向。