Video Deblurring by Sharpness Prior Detection and Edge Information

• URL: http://arxiv.org/abs/2501.12246v1
• Date: Tue, 21 Jan 2025 16:07:32 GMT
• Title: Video Deblurring by Sharpness Prior Detection and Edge Information
• Authors: Yang Tian, Fabio Brau, Giulio Rossolini, Giorgio Buttazzo, Hao Meng,
• Abstract summary: Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurring. This work introduces GoPro Random Sharp (GoProRS), a new dataset where the the frequency of sharp frames within the sequence is customizable. It presents a novel video deblurring model, called SPEINet, that integrates sharp frame features into blurry frame reconstruction.
• Score: 13.02974961130789
• License:
• Abstract: Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introducing artifacts and leading to poor results. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurring. However, existing datasets rely on fixed number of sharp frames, which may be too restrictive for some applications and may introduce a bias during model training. To address these limitations and enhance domain adaptability, this work first introduces GoPro Random Sharp (GoProRS), a new dataset where the the frequency of sharp frames within the sequence is customizable, allowing more diverse training and testing scenarios. Furthermore, it presents a novel video deblurring model, called SPEINet, that integrates sharp frame features into blurry frame reconstruction through an attention-based encoder-decoder architecture, a lightweight yet robust sharp frame detection and an edge extraction phase. Extensive experimental results demonstrate that SPEINet outperforms state-of-the-art methods across multiple datasets, achieving an average of +3.2% PSNR improvement over recent techniques. Given such promising results, we believe that both the proposed model and dataset pave the way for future advancements in video deblurring based on the detection of sharp frames.

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