Deep Image Deblurring: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Builds on 74 of its 176 references that are in the map. First posted on arXiv 26 Jan 2022.
Builds on
74 of 176 references.
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50 most cited of 74 All 74 (JSON)
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Citing papers per year: 2022: 12; 2023: 27; 2024: 28; 2025: 18; 2026: 9.
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