Blind Image Deblurring: a Review
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Abstract (arXiv)
This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurring. Finally, we introduce some typical optimization-based methods and learning-based methods.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 60 references that are in the map. First posted on arXiv 22 Jan 2022.
Builds on
11 of 60 references.
References · 11
- 2016Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurringcited by 722
- 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
- 2017DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networkscited by 366
- 2018Scale-recurrent Network for Deep Image Deblurringcited by 332
- 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
- 2015Learning a Convolutional Neural Network for Non-uniform Motion Blur Removalcited by 148
- 2016From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blurcited by 81
- 2016A Neural Approach to Blind Motion Deblurringcited by 69
- 2014Learning to Deblurcited by 66
- 2015Generalized Video Deblurring for Dynamic Scenescited by 40
- 2014Recent Progress in Image Deblurringcited by 7
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