Skip to main content
SpringerLink
Log in

A multi-path attention network for non-uniform blind image deblurring

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In the field of computer vision, image deblurring is a crucial and difficult task. By learning features from receptive fields, existing image deblurring algorithms have progressed. However, non-local feature representations, which depict the global data distribution of blurry images are not taken into account. As a result, in the local receptive field, combining the reliance of global space and the interaction of neighborhood space. For non-uniform deblurring, we develop a multi-path attention block (MPAB). To fuse several multi-path attention blocks, we offer an improved one-shot aggregation (IOSA). In addition, multiple loss functions are proposed to enhance network training and encourage convergence. Subjective and objective comparison experiments on various datasets are done to illustrate the efficiency of the suggested strategy. On synthetic datasets and real photos, our technique outperforms state-of-the-art (SOTA) methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Berahmand K, Bouyer A, Samadi N (2020) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101 (11):1711–1733

    Article  MathSciNet  MATH  Google Scholar 

  2. Chakrabarti A (2016) A neural approach to blind motion deblurring. In: ECCV, pp 221–235

  3. Cho S, Lee S (2009) Fast motion deblurring. ACM SIGGRAPH Asia, 1–8

  4. Gong D, Yang J, Liu L, Zhang Y et al (2017) From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In: CVPR, pp 3806–3815

  5. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In: NIPS, pp 2672–2680

  6. Gulrajani I, Ahmed F, Arjovsky M et al (2017) Improved training of Wasserstein GANs. ArXiv, arXiv:1704.00028

  7. Hradiš M, Kotera J, Zemcík P, Šroubek F (2015) Convolutional neural networks for direct text deblurring. Proceedings of BMVC, 10(2)

  8. Hu J, Shen L, Albanie S et al (2018) Squeeze-and-excitation networks. In: CVPR, pp 7132–7141

  9. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR, pp 2261–2269

  10. Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: CVPR, pp 1125–1134

  11. Jin M, Hirsch M, Favaro P (2018) Learning face deblurring fast and wide. In: CVPR workshops, pp 745–753

  12. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: ECCV, pp 694–711

  13. Kim TH, Ahn B, Lee KM (2013) Dynamic scene deblurring. In: CVPR, pp 3160–3167

  14. Kim TH, Lee KM (2014) Segmentation-free dynamic scene deblurring. In: CVPR, pp 2766–2773

  15. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  16. Köhler R, Hirsch M, Mohler B, Schölkopf B, Harmeling S (2012) Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: ECCV, pp 27–40

  17. Kupyn O, Budzan V, Mykhailych M, Mishkin D, Matas J (2018) Deblurgan: blind motion deblurring using conditional adversarial networks. In: CVPR, pp 8183–8192

  18. Kupyn O, Martyniuk T, Wu J, Wang ZY (2019) DeblurGAN-v2: deblurring (Orders-of-Magnitude) Faster and Better. In: ICCV, pp 8878–8887

  19. Lai WS, Huang JB, Hu Z, Ahuja N, Yang MH (2016) A comparative study for single image blind deblurring. In: CVPR, pp 1701–1709

  20. Lee Y, Hwang J, Lee S et al (2019) An energy and gpu-computation efficient backbone network for real-time object detection. In: CVPR

  21. Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recog 98:107038

    Article  Google Scholar 

  22. Li C, Cong R, Hou J, Zhang S, Qian Y, Kwong S (2019) Nested network with twostream pyramid for salient object detection in optical remote sensing images. arXiv:1906.08462

  23. Li C, Guo J, Guo C (2018) Emerging from water: underwater image color correction based on weakly supervised color transfer. SPL 25:323–327

    Google Scholar 

  24. Li C, Guo C, Guo J, Han P, Fu H, Cong R (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25(12):5664–5677

    Article  MathSciNet  MATH  Google Scholar 

  25. Li C, Guo C, Guo JC, Han P, Fu HZ, Cong R (2020) PDR-Net: perception-inspired single image Dehazing network with refinement. IEEE Trans Multimed 22:704–716

    Article  Google Scholar 

  26. Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019) An underwater image enhancement benchmark dataset and beyond. arXiv:1901.05495

  27. Li Y, Luo Y, Lu J (2021) Single image deblurring using bi-attention network. In: International Conference on Pattern Recognition (ICPR), pp 5333–5339

  28. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML, p 3

  29. Mao X, Shen C, Yang YB (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: NIPS, pp 2802–2810

  30. Mustaniemi J, Kannala J, Sarkka S et al (2018) Gyroscope-aided motion deblurring with deep networks. arXiv:1810.00986. Computer Vision and Pattern Recognition

  31. Nah S, Kim TH, Lee KM (2017) Deep multi-scale convolutional neural network for dynamic scene deblurring. In: CVPR, pp 257–265

  32. Nimisha TM, Sunil K, Rajagopalan A (2018) Unsupervised class-specific deblurring. In: ECCV, pp 353–369

  33. Pan J, Hu Z, Su Z, Lee H-Y, Yang M-H (2016) Soft-segmentation guided object motion deblurring. In: CVPR, pp 459–468

  34. Pan J, Sun D, Pfister H, Yang MH (2016) Blind image deblurring using dark channel prior. In: CVPR, pp 1628-1636

  35. Qi Q, Guo J, Jin W (2020) attention network for non-Uniform Deblurring. IEEE Access 8:100044–100057

    Article  Google Scholar 

  36. Qing Q (2021) Image fine-grained for non-uniform scenes deblurring. Artif Intell Commun Netw 101(11):1C13

    Google Scholar 

  37. Ren W, Cao X, Pan J, Guo X, Zuo W, Yang MH (2016) Image deblurring via enhanced low-rank prior. TIP 25:3426–3437

    MathSciNet  MATH  Google Scholar 

  38. Rostami M, Berahmand K, Forouzandeh S (2021) A novel community detection based genetic algorithm for feature selection. Journal of Big Data 8(1):1–27

    Article  Google Scholar 

  39. Rostami M, Berahmand K, Nasiri E et al (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100:104210

    Article  Google Scholar 

  40. Rostami M, Forouzandeh S, Berahmand K et al (2020) Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics 112(6):4370–4384

    Article  Google Scholar 

  41. Schuler CJ, Hirsch M, Harmeling S, Schölkopf B. (2016) Learning to deblur. TPAMI 38(7):1439–1451

    Article  Google Scholar 

  42. Sun J, Cao W, Xu Z, Ponce J (2015) Learning a convolutional neural network for non-uniform motion blur removal. In: CVPR, pp 769–777

  43. Sun L, Cho S, Wang J, Hays J (2013) Edge-based blur kernel estimation using patch priors. In: ICCP, pp 1–8

  44. Tao X, Gao H, Shen X, Wang J, Jia J (2018) Scale-recurrent network for deep image deblurring. In: CVPR, pp 8174C8182

  45. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. TIP 13:600–612

    Google Scholar 

  46. Wang Q, Han T, Qin Z et al (2020) Multi-task attention network for lane detection and fitting. IEEE Trans Neural Netw Learn Syst 33(3):1066–1078. https://doi.org/10.1109/TNNLS.2020.3039675

    Article  Google Scholar 

  47. Xie S, Girshick R, Dollr P et al (2017) Aggregated residual transformations for deep neural networks. In: CVPR, pp 1492–1500

  48. Xu L, Jia J (2010) Two-phase kernel estimation for robust motion deblurring. In: ECCV, pp 157–170

  49. Xu L, Zheng S, Jia J (2013) Unnatural l0 sparse representation for natural image deblurring. In: CVPR, pp 1107–1114

  50. Yuan Y, Xiong Z, Wang Q (2019) VSSA-NET: vertical spatial sequence attention network for traffic sign detection. IEEE Trans Image Process 28 (7):3423–3434

    Article  MathSciNet  MATH  Google Scholar 

  51. Zhang Z, Chen H, Yin X et al (2021) Joint generative image deblurring aided by edge attention prior and dynamic kernel selection. Wireless Communications & Mobile Computing

  52. Zhang H, Yang J, Zhang Y, Huang TS (2011) Sparse representation based blind image deblurring. In: ICME, pp 1–6

  53. Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp 2223C2232

Download references

Author information

Authors and Affiliations

  1. School of Physics and Electronic Information Engineering, Qinghai Nationalities University, Bayi Middle Road 3, 810007, Xining, China

    Qing Qi

Authors
  1. Qing Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Qi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, Q. A multi-path attention network for non-uniform blind image deblurring. Multimed Tools Appl 82, 36909–36928 (2023). https://doi.org/10.1007/s11042-023-14470-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14470-6

Keywords

Search

Navigation