Abstract
Video deblurring is a critical task in computer vision, aimed at restoring the clarity of videos distorted by motion blur or other factors. It holds immense importance across various domains, including surveillance, entertainment, medical imaging, and autonomous driving, where clear visual information is crucial for decision-making and analysis. This article provides an overview of recent advancements in video deblurring techniques, ranging from convolutional neural network (CNN)-based methods to Transformer-based models and event-based reconstruction approaches. By synthesizing insights from recent research, this review delves into the applications and methodologies of these techniques, showcasing their effectiveness in real-world scenarios. By fostering knowledge exchange and inspiring further advancements in the field, this review aims to contribute to the continuous improvement of video processing technologies for enhanced visual quality and analysis. Keywords: Video Deblurring, Video Restoration, deep learning, Convolutional Neural Networks, Event Based Reconstruction, Transformers, Image Processing
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