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Neural Computing and Applications

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21-02-2024 | Original Article

Deep non-blind deblurring network for saturated blurry images

Authors: Bo Fu, Shilin Fu, Yuechu Wu, Yuanxin Mao, Yonggong Ren, Dang N. H. Thanh

Published in: Neural Computing and Applications | Issue 14/2024

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Abstract

Non-blind image deblurring has attracted a lot of attention in the field of low-level vision. However, the existing non-blind deblurring methods cannot effectively deal with a saturated blurry image. The key point is that the degradation model of saturated blurry images does not satisfy the linear convolution model of a conventional blurry image. To solve the problem, in this paper, we proposed a novel deep non-blind deblurring method, dubbed saturated image non-blind deblurring network(SDBNet). The SDBNet contains two trainable sub-network, i.e., confident estimate network (CEN) and detail enhance network (DEN). Specifically, the SDBNet uses CEN to estimate the confidence map for the saturated blurry image, which is used to recognize saturated pixels in the blurry image, and then uses the confidence map, and blur kernel to restore the blurry image. Finally, we use DEN to enhance the edges and textures of the restored image. We first pre-train CEN and DEN. In order to effectively pre-train CEN, we propose a new robust function, which is used to generate label data for CEN. The experimental results show that compared with several existing non-blind deblurring methods, SDBNet can effectively restore saturated blurry images and better restore the texture, edge, and other structural information of blurry images.

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Cheng S, Liu R, He Y, Fan X, Luo Z (2020) Blind image deblurring via hybrid deep priors modeling. Neurocomputing 387:334–345CrossRef

Javaran TA, Hassanpour H, Abolghasemi V (2017) Non-blind image deconvolution using a regularization based on re-blurring process. Comput Vis Image Underst 154:16–34CrossRef

Wang W, Su C (2022) An optimization method for motion blur image restoration and ringing suppression via texture mapping. ISA Trans 131:650–661CrossRef

Richardson WH (1972) Bayesian-based iterative method of image restoration. JOSA 62(1):55–59CrossRef

Wiener N, Wiener N, Mathematician C, Wiener N, Wiener N, Mathématicien C (1949) Extrapolation, interpolation, and smoothing of stationary time series: with engineering applications, vol. 113, no. 21

Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-laplacian priors. In: NeurIPS, pp 1033–1041

Chan SH, Wang X, Elgendy OA (2017) Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE Trans Comput 3(1):84–98MathSciNet

Schmidt U, Rother C, Nowozin S, Jancsary J, Roth S (2013) Discriminative non-blind deblurring. In: IEEE CVPR, pp 604–611

Schmidt U, Jancsary J, Nowozin S, Roth S, Rother C (2016) Cascades of regression tree fields for image restoration. IEEE Trans Pattern Anal Mach Intell 38(4):677–689CrossRef

10.

Tappen MF, Liu C, Adelson EH, Freeman WT (2007) Learning gaussian conditional random fields for low-level vision. In: IEEE CVPR

11.

Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image restoration. In: IEEE ICCV, pp 479–486

12.

Chen Y, Yu W, Pock T (2015) On learning optimized reaction diffusion processes for effective image restoration. In: IEEE CVPR, pp 5261–5269

13.

Chen Y, Pock T (2017) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256–1272CrossRef

14.

Zhang K, Zuo W, Gu S, Zhang L (2017) Learning deep CNN denoiser prior for image restoration. In: IEEE CVPR, pp 3929–3938

15.

Zhang K, Li Y, Zuo W, Zhang L, Van Gool L, Timofte R (2021) Plug-and-play image restoration with deep denoiser prior. IEEE Trans Pattern Anal Mach Intell 44(10):6360–6376CrossRef

16.

Zhang J, Pan J, Lai W-S, Lau RW, Yang MH (2017) Learning fully convolutional networks for iterative non-blind deconvolution. In: IEEE CVPR, pp 3817–3825

17.

Chen L, Zhang J, Lin S, Fang F, Ren JS (2021) Blind deblurring for saturated images. In: IEEE CVPR, pp 6308–6316

18.

Cho S, Wang J, Lee S (2011) Handling outliers in non-blind image deconvolution. In: IEEE ICCV, pp 495–502

19.

Whyte O, Sivic J, Zisserman A (2014) Deblurring shaken and partially saturated images. Int J Comput Vision 110(2):185–201CrossRef

20.

Pan J, Lin Z, Su Z, Yang M (2016) Robust kernel estimation with outliers handling for image deblurring. In: IEEE CVPR, pp 2800–2808

21.

Zhang X, Wang R, Chen D, Zhao Y, Gao W (2021) Handling outliers by robust m-estimation in blind image deblurring. IEEE Trans Multimed 23:3215–3226CrossRef

22.

Dong J, Pan J (2021) Deep outlier handling for image deblurring. IEEE Trans Image Process 30:1799–1811CrossRef

23.

Chen L, Zhang J, Pan J, Lin S, Fang F, Ren JS (2021) Learning a non-blind deblurring network for night blurry images. In: IEEE CVPR, Vol. 10, pp 542–550

24.

Pan J, Sun D, Pfister H, Yang M (2018) Deblurring images via dark channel prior. IEEE Trans Pattern Anal Mach Intell 40(10):2315–2328CrossRef

25.

Chen L, Fang F, Wang T, Zhang G (2019) Blind image deblurring with local maximum gradient prior. In: IEEE CVPR, pp 1742–1750

26.

Shan Q, Jia J, Agarwala A (2008) High-quality motion deblurring from a single image. ACM Trans Graph 27(3):73CrossRef

27.

Pan J, Hu Z, Su Z, Yang M (2014) Deblurring text images via l0-regularized intensity and gradient prior. In: IEEE CVPR, pp 2901–2908

28.

Pan J, Hu Z, Su Z, Yang M-H (2016) \(l_0\)-regularized intensity and gradient prior for deblurring text images and beyond. IEEE Trans Pattern Anal Mach Intell 39(2):342–355CrossRef

29.

Xu L, Zheng S, Jia J (2013) Unnatural L0 sparse representation for natural image deblurring. In: IEEE CVPR, pp 1107–1114

30.

Ren W, Cao X, Pan J, Guo X, Zuo W, Yang M (2016) Image deblurring via enhanced low-rank prior. IEEE Trans Image Process 25(7):3426–3437MathSciNetCrossRef

31.

Yan Y, Ren W, Guo Y, Wang R, Cao X (2017) Image deblurring via extreme channels prior. In: IEEE CVPR, pp 6978–6986

32.

Rudin LI, Osher SJ (1994) Total variation based image restoration with free local constraints. In: IEEE ICIP, pp 31–35

33.

Wang Y, Yang J, Yin W, Zhang Y (2008) A new alternating minimization algorithm for total variation image reconstruction. SIAM J Imag Sci 1(3):248–272MathSciNetCrossRef

34.

Fortunato HE, Oliveira MM (2014) Fast high-quality non-blind deconvolution using sparse adaptive priors. Visual Comput 30(6–8):661–671CrossRef

35.

Schmidt U, Gao Q, Roth S (2010) A generative perspective on MRFS in low-level vision. In: IEEE CVPR, pp. 1751–1758

36.

Schmidt U, Roth S (2014) Shrinkage fields for effective image restoration. In: IEEE CVPR, pp 2774–2781

37.

Li L, Pan J, Lai W, Gao C, Sang N, Yang M (2019) Blind image deblurring via deep discriminative priors. Int J Comput Vision 127(8):1025–1043CrossRef

38.

Zhang J, Pan J, Lai W, Lau RWH, Yang M (2017) Learning fully convolutional networks for iterative non-blind deconvolution. In: IEEE CVPR, pp 6969–6977

39.

Gong D, Zhang Z, Shi Q, van den Hengel A, Shen C, Zhang Y (2020) Learning deep gradient descent optimization for image deconvolution. IEEE Trans Neural Netw Learn Syst 31(12):5468–5482MathSciNetCrossRef

40.

Dong J, Roth S, Schiele B (2021) Learning spatially-variant MAP models for non-blind image deblurring. In: IEEE CVPR, pp 4886–4895

41.

Wang D, Tang H, Pan J, Tang J (2021) Learning a tree-structured channel-wise refinement network for efficient image deraining. In: IEEE ICME, pp 1–6

42.

Wang D, Pan J, Tang J (2023) Single image deraining using residual channel attention networks. J Comput Sci Technol 38(2):439–454CrossRef

43.

Dong J, Pan J, Su Z, Yang M (2017) Blind image deblurring with outlier handling. In: IEEE ICCV, pp 2497–2505

44.

Hu Z, Cho S, Wang J, Yang M (2014) Deblurring low-light images with light streaks. In: IEEE CVPR, pp 3382–3389

45.

Wang D, Liu J, Liu R, Fan X (2023) An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection. Inf Fus 98:101828CrossRef

46.

Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: ECCV, vol. 11211, pp 294–310

47.

Levin A, Weiss Y, Durand F, Freeman W (2009) Understanding and evaluating blind deconvolution algorithms. In: IEEE CVPR, pp 1964–1971

48.

Dabov K, Foi A, Katkovnik V, Egiazarian KO (2008) Image restoration by sparse 3d transform-domain collaborative filtering. In: SPIE, vol. 6812, pp 681207

49.

Schuler CJ, Burger HC, Harmeling S, Schölkopf B (2013) A machine learning approach for non-blind image deconvolution. In: IEEE CVPR, pp 1067–1074

50.

Son H, Lee S (2017) Fast non-blind deconvolution via regularized residual networks with long/short skip-connections. In: IEEE ICCP, pp 23–32

51.

Liu Y, Lai W, Chen Y, Kao Y, Yang M, Chuang Y, Huang J (2020) Single-image HDR reconstruction by learning to reverse the camera pipeline. In: IEEE CVPR, pp 1648–1657

52.

Gharbi M, Chen J, Barron JT, Hasinoff SW, Durand F (2017) Deep bilateral learning for real-time image enhancement. ACM Trans Graph 36(4): 1–12

53.

Ma L, Ma T, Liu R, Fan X, Luo Z (2022) Toward fast, flexible, and robust low-light image enhancement. In: IEEE CVPR, pp 5627–5636

54.

Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R (2020) Nerf: representing scenes as neural radiance fields for view synthesis. In: Springer ECCV, vol. 12346, pp 405–421

55.

Zheng H, Yong H, Zhang L (2021) Deep convolutional dictionary learning for image denoising. In: IEEE CVPR, pp 630–641

56.

Zhang K, Gool LV, Timofte R (2020) Deep unfolding network for image super-resolution. In: IEEE CVPR, pp 3214–3223

Title: Deep non-blind deblurring network for saturated blurry images
Authors: Bo Fu
Shilin Fu
Yuechu Wu
Yuanxin Mao
Yonggong Ren
Dang N. H. Thanh
Publication date: 21-02-2024
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 14/2024
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-024-09495-3

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