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A Survey on Image Deblurring

arXiv 2202.07456 ↗First posted 31 Jan 2022cs.CVcited by 0

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Abstract (arXiv)

With the improvement of social life quality and the real needs of daily work, images are more and more all around us. Image blurring due to camera shake, human movement, etc. has become the key to affecting image quality. How to remove image blur and restore clear image has gradually become an important research direction in the field of computer vision. After more than half a century of unremitting efforts, the majority of scientific and technological workers have made fruitful progress in image deblurring. This article reviews the work of image deblurring and specifically introduces more classic image deblurring methods, which is helpful to understand current research and look forward to future trends. This article reviews the traditional image deblurring methods and depth-represented image deblurring methods, and comprehensively classifies and introduces the corresponding technical methods. This review can provide some guidance for researchers in the field of image deblurring, and at the same time facilitate their subsequent study and research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 53 references that are in the map. First posted on arXiv 31 Jan 2022.

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References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2017Non-local Neural Networkscited by 2,290
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  11. 2018Scale-recurrent Network for Deep Image Deblurringcited by 332
  12. 2019Deep Stacked Hierarchical Multi-patch Network for Image Deblurringcited by 205
11 more
  1. 2020Deblurring by Realistic Blurringcited by 187
  2. 2020Human-Aware Motion Deblurringcited by 174
  3. 2015Learning a Convolutional Neural Network for Non-uniform Motion Blur Removalcited by 148
  4. 2020Strip Pooling: Rethinking Spatial Pooling for Scene Parsingcited by 114
  5. 2019Neural Blind Deconvolution Using Deep Priorscited by 113
  6. 2019Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Trainingcited by 112
  7. 2020Cascaded Deep Video Deblurring Using Temporal Sharpness Priorcited by 85
  8. 2016A Neural Approach to Blind Motion Deblurringcited by 69
  9. 2021BANet: Blur-aware Attention Networks for Dynamic Scene Deblurringcited by 44
  10. 2013Revisiting Bayesian Blind Deconvolutioncited by 7
  11. 2019A New Few-shot Segmentation Network Based on Class Representationcited by 4

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