Weber's Law-based Regularization for Blind Image Deblurring
Received: 30 October 2023 | Revised: 28 November 2023 | Accepted: 1 December 2023 | Online: 8 February 2024
Corresponding author: Malik Najmus Saqib
Abstract
Blind image deblurring aims to recover an output latent image and a blur kernel from a given blurred image. Kernel estimation is a significant step in blind image deblurring and requires a regularization technique to minimize the cost function and the edges of objects to generate a sharp image in a better way. This study proposes a new image regularization technique called Weber's Law Regularization (WLR) based on the Weber law phenomenon. The Weber ratio was used to preserve the edges of small salient objects and to minimize the cost function to obtain a sharp image while minimizing the ringing effect. To validate the WLR, experiments were conducted on benchmark synthetic and real word images and compared with existing state-of-the-art methods. The experimental results showed that WLR can effectively and efficiently deblur images even in the absence of prior knowledge.
Keywords:
regularization, image deblurring, Weber's law, Weber's Law Regularization (WLR)Downloads
References
S. D. Babacan, R. Molina, M. N. Do, and A. K. Katsaggelos, "Bayesian Blind Deconvolution with General Sparse Image Priors," in Computer Vision – ECCV 2012, Florence, Italy, 2012, pp. 341–355.
D. Wipf and H. Zhang, "Analysis of Bayesian Blind Deconvolution," in Energy Minimization Methods in Computer Vision and Pattern Recognition, Lund, Sweden, 2013, pp. 40–53.
S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Variational Bayesian Blind Deconvolution Using a Total Variation Prior," IEEE Transactions on Image Processing, vol. 18, no. 1, pp. 12–26, Jan. 2009.
D. Wipf and H. Zhang, "Revisiting Bayesian Blind Deconvolution," Journal of Machine Learning Research, vol. 15, no. 111, pp. 3775–3814, 2014.
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, "Efficient marginal likelihood optimization in blind deconvolution," in CVPR 2011, Colorado Springs, CO, USA, Jun. 2011, pp. 2657–2664.
R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, "Removing camera shake from a single photograph," in ACM SIGGRAPH 2006 Papers, Boston, MA, USA, Apr. 2006, pp. 787–794.
L. Guo, X.-L. Zhao, X. M. Gu, Y. L. Zhao, Y. B. Zheng, and T. Z. Huang, "Three-dimensional fractional total variation regularized tensor optimized model for image deblurring," Applied Mathematics and Computation, vol. 404, Art. no. 126224, Sep. 2021.
S. Cho and S. Lee, "Fast motion deblurring," in ACM SIGGRAPH Asia 2009 papers, Yokohama, Japan, Sep. 2009, pp. 1–8.
Q. Shan, J. Jia, and A. Agarwala, "High-quality motion deblurring from a single image," ACM Transactions on Graphics, vol. 27, no. 3, pp. 1–10, May 2008.
L. Xu, S. Zheng, and J. Jia, "Unnatural L0 Sparse Representation for Natural Image Deblurring," in 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, Jun. 2013, pp. 1107–1114.
D. Krishnan, T. Tay, and R. Fergus, "Blind deconvolution using a normalized sparsity measure," in CVPR 2011, Colorado Springs, CO, USA, Jun. 2011, pp. 233–240.
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, "Understanding and evaluating blind deconvolution algorithms," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, Jun. 2009, pp. 1964–1971.
D. Perrone and P. Favaro, "Total Variation Blind Deconvolution: The Devil Is in the Details," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, Jun. 2014, pp. 2909–2916.
T. Yue, S. Cho, J. Wang, and Q. Dai, "Hybrid Image Deblurring by Fusing Edge and Power Spectrum Information," in Computer Vision – ECCV 2014, Zurich, Switzerland, 2014, pp. 79–93.
D. Krishnan, J. Bruna, and R. Fergus, "Blind Deconvolution with Non-local Sparsity Reweighting." arXiv, Jun. 16, 2014.
W. Zuo, D. Ren, S. Gu, L. Lin, and L. Zhang, "Discriminative learning of iteration-wise priors for blind deconvolution," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 3232–3240.
L. Li, J. Pan, W. S. Lai, C. Gao, N. Sang, and M.-H. Yang, "Learning a Discriminative Prior for Blind Image Deblurring," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, Jun. 2018, pp. 6616–6625.
W. S. Lai, J. J. Ding, Y. Y. Lin, and Y. Y. Chuang, "Blur kernel estimation using normalized color-line priors," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 64–72.
L. Sun, S. Cho, J. Wang, and J. Hays, "Edge-based blur kernel estimation using patch priors," in IEEE International Conference on Computational Photography (ICCP), Cambridge, MA, USA, Apr. 2013, pp. 1–8.
J. Pan, Z. Hu, Z. Su, and M. H. Yang, "Deblurring Face Images with Exemplars," in Computer Vision – ECCV 2014, Zurich, Switzerland, 2014, pp. 47–62.
L. Xu and J. Jia, "Two-Phase Kernel Estimation for Robust Motion Deblurring," in Computer Vision – ECCV 2010, Heraklion, Greece, 2010, pp. 157–170.
Y. Zhou and N. Komodakis, "A MAP-Estimation Framework for Blind Deblurring Using High-Level Edge Priors," in Computer Vision – ECCV 2014, Zurich, Switzerland, 2014, pp. 142–157.
W. Zuo, D. Ren, D. Zhang, S. Gu, and L. Zhang, "Learning Iteration-wise Generalized Shrinkage–Thresholding Operators for Blind Deconvolution," IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1751–1764, Apr. 2016.
U. Schmidt, C. Rother, S. Nowozin, J. Jancsary, and S. Roth, "Discriminative Non-blind Deblurring," in 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, Jun. 2013, pp. 604–611.
T. Michaeli and M. Irani, "Blind Deblurring Using Internal Patch Recurrence," in Computer Vision – ECCV 2014, Zurich, Switzerland, 2014, pp. 783–798.
T. F. Chan and C. K. Wong, "Total variation blind deconvolution," IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 370–375, Mar. 1998.
L. Zhong, S. Cho, D. Metaxas, S. Paris, and J. Wang, "Handling Noise in Single Image Deblurring Using Directional Filters," in 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, Jun. 2013, pp. 612–619.
H. Zhang, J. Yang, Y. Zhang, and T. S. Huang, "Sparse representation based blind image deblurring," in 2011 IEEE International Conference on Multimedia and Expo, Barcelona, Spain, Jul. 2011, pp. 1–6.
H. Li, Y. Zhang, H. Zhang, Y. Zhu, and J. Sun, "Blind image deblurring based on sparse prior of dictionary pair," in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan, Aug. 2012, pp. 3054–3057.
X. Tao, H. Gao, X. Shen, J. Wang, and J. Jia, "Scale-Recurrent Network for Deep Image Deblurring," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, Jun. 2018, pp. 8174–8182.
D. Krishnan and R. Fergus, "Fast Image Deconvolution using Hyper-Laplacian Priors," in Advances in Neural Information Processing Systems, 2009, vol. 22.
J. Li and W. Lu, "Blind image motion deblurring with L0-regularized priors," Journal of Visual Communication and Image Representation, vol. 40, pp. 14–23, Oct. 2016.
G. Marti, B. Ma, and H.-A. Loeliger, "Why Maximum-A-Posteriori Blind Image Deblurring Works After All," in 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland, Dec. 2021, pp. 666–670.
J. Dong, S. Roth, and B. Schiele, "Learning Spatially-Variant MAP Models for Non-blind Image Deblurring," in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, Jun. 2021, pp. 4884–4893.
C. Li, "A Survey on Image Deblurring." arXiv, Jan. 30, 2022.
F. J. Tsai, Y. T. Peng, Y. Y. Lin, C. C. Tsai, and C. W. Lin, "Stripformer: Strip Transformer for Fast Image Deblurring," in Computer Vision – ECCV 2022, Tel Aviv, Israel, 2022, pp. 146–162.
Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li, "Uformer: A General U-Shaped Transformer for Image Restoration," in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, Jun. 2022, pp. 17662–17672.
O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better," presented at the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Oct. 2019, pp. 8877–8886.
L. Chen et al., "Deep Richardson–Lucy Deconvolution for Low-Light Image Deblurring," International Journal of Computer Vision, Sep. 2023.
M. Asim, F. Shamshad, and A. Ahmed, "Blind Image Deconvolution Using Deep Generative Priors," IEEE Transactions on Computational Imaging, vol. 6, pp. 1493–1506, 2020.
S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022.
P. Matlani and M. Shrivastava, "An Efficient Algorithm Proposed For Smoke Detection in Video Using Hybrid Feature Selection Techniques," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3939–3944, Apr. 2019.
A. Khalfa, M. Sahed, E. Kenane, and N. Amardjia, "A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9680–9686, Dec. 2022.
J. Pan, D. Sun, H. Pfister, and M.-H. Yang, "Deblurring Images via Dark Channel Prior," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 10, pp. 2315–2328, Jul. 2018.
Y. Yan, W. Ren, Y. Guo, R. Wang, and X. Cao, "Image Deblurring via Extreme Channels Prior," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 6978–6986.
J. Dong, J. Pan, Z. Su, and M. H. Yang, "Blind Image Deblurring with Outlier Handling," in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Jul. 2017, pp. 2497–2505.
H. Dawood et al., "Probability weighted moments regularization based blind image De-blurring," Multimedia Tools and Applications, vol. 79, no. 7, pp. 4483–4498, Feb. 2020.
L. Chen, F. Fang, T. Wang, and G. Zhang, "Blind Image Deblurring With Local Maximum Gradient Prior," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 2019, pp. 1742–1750.
Y. Bai, G. Cheung, X. Liu, and W. Gao, "Graph-Based Blind Image Deblurring From a Single Photograph," IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1404–1418, Mar. 2019.
D. Yang, X. Wu, and H. Yin, "Blind Image Deblurring via a Novel Sparse Channel Prior," Mathematics, vol. 10, no. 8, Art. no. 1238, Jan. 2022.
J. Chen et al., "WLD: A Robust Local Image Descriptor," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1705–1720, Sep. 2010.
H. Dawood, H. Dawood, and P. Guo, "Removal of high-intensity impulse noise by Weber’s law Noise Identifier," Pattern Recognition Letters, vol. 49, pp. 121–130, Nov. 2014.
A. Beck and M. Teboulle, "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems," SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, Jan. 2009.
N. Bibi and H. Dawood, "SEBR: Scharr Edge-Based Regularization Method for Blind Image Deblurring," Arabian Journal for Science and Engineering, Jun. 2023.
Downloads
How to Cite
License
Copyright (c) 2024 Malik Najmus Saqib, Hussain Dawood, Ahmed Alghamdi, Hassan Dawood
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
