mlsanityData to 7 Oct 2026

A Comprehensive Survey on Deep Neural Image Deblurring

arXiv 2310.04719 ↗First posted 7 Oct 2023cs.CV eess.IVcited by 2

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Abstract (arXiv)

Image deblurring tries to eliminate degradation elements of an image causing blurriness and improve the quality of an image for better texture and object visualization. Traditionally, prior-based optimization approaches predominated in image deblurring, but deep neural networks recently brought a major breakthrough in the field. In this paper, we comprehensively review the recent progress of the deep neural architectures in both blind and non-blind image deblurring. We outline the most popular deep neural network structures used in deblurring applications, describe their strengths and novelties, summarize performance metrics, and introduce broadly used datasets. In addition, we discuss the current challenges and research gaps in this domain and suggest potential research directions for future works.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 76 of its 173 references that are in the map. First posted on arXiv 7 Oct 2023.

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76 of 173 references.

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50 most cited of 76 All 76 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

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