Abstract
Deblurring an image has been a long researched problem. This problem is very complex due to the lack of sufficient information about the blur parameters. Image deblurring is important in applications such as remote sensing where the same image scene comprising of moving objects cannot be captured again. Since the captured image is the only known quantity, from which the blur parameters affecting it and the sharp image has to be estimated it is an illposed problem. In this paper we present a novel image deblurring algorithm which makes use of region specific priors and techniques for image deblurring. This is based on the idea that different image regions require different techniques for effective image deblurring. Applying the same technique to deblur the entire image results in a deblurred image which is sharp in only some regions whereas some regions are not effectively deblurred or have ringing artifacts. The proposed method makes use of a l1 relaxed l0 prior on the sharp edges of the image to effectively enhance the true edges of the image. An l1/l2 norm on the low peaks to avoid blurring while retaining the true values in these regions and lp norm prior on the uniform regions to avoid the generation of ringing artifacts. On an average there is about 18% increase in PSNR values and a 5% increase in the SSIM values in comparison with the existing state of the art methods.
Similar content being viewed by others
References
Adam T, Paramesran R, Mingming Y, Ratnavelu K (2021) Combined higher order non-convex total variation with overlapping group sparsity for impulse noise removal. Multimed Tools Appl 80(12):18503
Anwar S, Huynh CP, Porikli F (2018) Image deblurring with a class-specific prior. IEEE transactions on pattern analysis and machine intelligence 41 (9):2112
Babacan SD, Molina R, Do MN, Katsaggelos AK (2012) Bayesian blind deconvolution with general sparse image priors. In: European conference on computer vision. Springer, pp 341–355
Bai Y, Jia H, Jiang M, Liu X, Xie X, Gao W (2019) IEEE transactions on circuits and systems for video technology
Batra D, Yadollahpour P, Guzman-Rivera A, Shakhnarovich G (2012) Diverse m-best solutions in markov random fields. In: European conference on computer vision. Springer, pp 1–16
Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183
Chan TF, Wong CK (1998) Total variation blind deconvolution. IEEE Trans Image Process 7(3):370
Chen L, Zhang H, Ren D, Zhang D, Zuo W (2014) Medical image fusion by combining nonsubsampled contourlet transform and sparse representation, vol 484, pp 12–21. https://doi.org/10.1007/978-3-662-45643-9_2
Cho S, Lee S (2009) Fast motion deblurring. ACM Trans Graph (TOG) 28(5):145
Elmi Y, Zargari F, Rahmani AM (2020) Iterative approach for parametric PSF estimation. Multimed Tools Appl 79(39):29433
Fang X, Zhou Q, Shen J, Jacquemin C, Shao L (2018) IEEE transactions on cybernetics
Feng M, Mitchell JE, Pang JS, Shen X, Wächter A (2013) Industrial engineering and management sciences. Technical report. Northwestern University, Evanston, IL, USA
Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) In: ACM SIGGRAPH 2006 papers, pp 787–794
Figueiredo MA, Nowak RD (2003) An EM algorithm for wavelet-based image restoratio. IEEE Trans Image Process 12(8):906
Ge X, Tan J, Zhang L (2021) IEEE transactions on image processing
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell, (6), p 721
Gribonval R, Nielsen M (2003) Sparse representations in unions of bases. IEEE Trans Inf Theory 49(12):3320
He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341
Javaran TA, Hassanpour H, Abolghasemi V (2019) Blind motion image deblurring using an effective blur kernel prior. Multimed Tools Appl 78 (16):22555
Kotera J, Matas J, Šroubek F (2020) Restoration of fast moving objects. IEEE Trans Image Process 29:8577
Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-Laplacian priors. In: Advances in neural information processing systems, pp 1033–1041
Krishnan D, Tay T, Fergus R (2011) In: CVPR 2011. IEEE, pp 233–240
Levin A, Weiss Y, Durand F, Freeman WT (2009) In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 1964–1971
Li J, Lu W (2016) Blind image motion deblurring with L0-regularized priors. J Vis Commun Image Represent 40:14
Lim H, Yu S, Park K, Seo D, Paik J (2020) Texture-Aware Deblurring for Remote Sensing Images Using ℓ0-Based Deblurring and ℓ2-Based Fusion. IEEE J Select Topics Appl Earth Observations Remote Sensing 13:3094
Lin Y, Kandel Y, Zotta M, Lifshin E (2016) In: 2016 IEEE southwest symposium on image analysis and interpretation (SSIAI). IEEE, pp 33–36
Liu Y, Dong W, Gong D, Zhang L, Shi Q (2018) In: Proceedings of the European conference on computer vision (ECCV), pp 452–468
Liu H, Tan S (2018) Image regularizations based on the sparsity of corner points. IEEE Trans Image Process 28(1):72
Liu J, Yan M, Zeng T (2019) IEEE transactions on pattern analysis and machine intelligence
McGaffin MG, Fessler JA (2015) Edge-preserving image denoising via group coordinate descent on the GPU. IEEE Trans Image Process 24(4):1273
Mohd Shapri AH, Abdullah MZ (2017) Accurate retrieval of region of interest for estimating point spread function and image deblurring. Imaging Sci J 65(6):327
Palmer JA, Kreutz-Delgado K, Makeig S (2010) Strong sub-and super-gaussianity. In: International conference on latent variable analysis and signal separation. Springer, pp 303–310
Pan J, Sun D, Pfister H, Yang MH (2017) Deblurring images via dark channel prior. IEEE Trans Pattern Anal Mach Intell 40(10):2315
Perrone D, Favaro P (2015) A clearer picture of total variation blind deconvolution. IEEE Trans Pattern Anal Mach Intell 38(6):1041
Poobathy D, Chezian RM, Image IJ (2014) Edge detection operators: peak signal to noise ratio based comparison. Graph Signal Process 10:55
Portilla J, Tristan-Vega A, Selesnick IW (2015) Efficient and robust image restoration using multiple-feature L2-relaxed sparse analysis priors. IEEE Trans Image Process 24(12):5046
Rameshan RM, Chaudhuri S, Velmurugan R (2012) In: proceedings of the eighth indian conference on computer vision, graphics and image processing, pp 1–7
Ramirez C, Kreinovich V, Argaez M (2013) Industrial engineering and management sciences. Technical report. Northwestern University, Evanston, IL, USA
Roth S, Black MJ (2009) Fields of experts. Int J Comput Vis 82(2):205
Salau AO (2020) Recent trends in image and signal processing in computer vision. Springer, pp 19–32
Salau AO, Jain S (2019) Feature extraction: a survey of the types, techniques, applications. In: 2019 International conference on signal processing and communication (ICSC). IEEE, pp 158–164
Salau AO, Yesufu TK, Ogundare BS (2021) Vehicle plate number localization using a modified GrabCut algorithm. J King Saud Univ-Comput Inf Sci 33(4):399
Satish P, Srikantaswamy M, Ramaswamy NK (2020) A comprehensive review of blind deconvolution techniques for image deblurring. Traitment Du Signal 37(3):527
Schuler CJ, Hirsch M, Harmeling S, Schölkopf B (2016) Learning to deblur. IEEE Trans Pattern Anal Mach Intell 38(7):1439
Sha L, Schonfeld D, Wang J (2019) IEEE transactions on circuits and systems for video technology
Shan Q, Jia J, Agarwala A (2008) High-quality motion deblurring from a single image. Acm Trans Graph (tog) 27(3):1
Shrivakshan G, Chandrasekar C (2012) A comparison of various edge detection techniques used in image processing. Int J Comput Sci Issues (IJCSI) 9 (5):269
Sun T, Barrio R, Rodríguez M, Jiang H (2019) Inertial nonconvex alternating minimizations for the image deblurring. IEEE Trans Image Process 28 (12):6211
Šroubek F, Šmídl V, Kotera J (2014) In: 2014 IEEE international conference on image processing (ICIP). IEEE, pp 4492–4496
Tikhonov AN, Arsenin VY (1977) Great falls, MT USA: Winston
Vijayarani S, Vinupriya M (2013) Performance analysis of canny and sobel edge detection algorithms in image mining. Int J Innovative Res Comput Commun Eng 1(8):1760
Weiss Y, Yanover C, Meltzer T (2012) MAP estimation, linear programming and belief propagation with convex free energies. arXiv:1206.5286
Whyte O, Sivic J, Zisserman A (2014) Deblurring shaken and partially saturated images. Int J Comput Vis 110(2):185
Xu L, Jia J (2010) In: European conference on computer vision. Springer, pp 157–170
Xu L, Zheng S, Jia J (2013) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1107–1114
Yan Y, Ren W, Guo Y, Wang R, Cao X (2017) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4003–4011
You YL, Kaveh M (1999) Blind image restoration by anisotropic regularization. IEEE Trans Image Process 8(3):396
Yu J, Chang Z, Xiao C (2019) Multimedia tools and applications. Springer 78(13):18549
Zha Z, Yuan X, Zhou J, Zhu C, Wen B (2020) Image restoration via simultaneous nonlocal self-similarity priors. IEEE Trans Image Process 29:8561
Zuo W, Ren D, Zhang D, Gu S, Zhang L (2016) Learning iteration-wise generalized shrinkage?thresholding operators for blind deconvolution. IEEE Trans Image Process 25(4):1751
Acknowledgements
The authors would like to thank VGST K-FIST L2 for sponsored “Establishment of Renewable Smart Grid Laboratory”, JSS Academy of Technical Education, K.S. Institute of Technology and VTU for their constant support
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
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.
About this article
Cite this article
S, P., S, M. & N, S. Image region driven prior selection for image deblurring. Multimed Tools Appl 82, 24181–24202 (2023). https://doi.org/10.1007/s11042-023-14335-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14335-y