Year: 2017
Communications in Computational Physics, Vol. 22 (2017), Iss. 3 : pp. 803–828
Abstract
Multiplicative noise removal is a challenging problem in image restoration. In this paper, by applying Box-Cox transformation, we convert the multiplicative noise removal problem into the additive noise removal problem and the block matching three dimensional (BM3D) method is applied to get the final recovered image. Indeed, BM3D is an effective method to remove additive Gaussian white noise in images. A maximum likelihood method is designed to determine the parameter in the Box-Cox transformation. We also present the unbiased inverse transform for the Box-Cox transformation which is important. Both theoretical analysis and experimental results illustrate clearly that the proposed method can remove multiplicative noise very well especially when multiplicative noise is heavy. The proposed method is superior to the existing methods for multiplicative noise removal in the literature.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2016-0074
Communications in Computational Physics, Vol. 22 (2017), Iss. 3 : pp. 803–828
Published online: 2017-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 26
-
Cauchy Noise Removal via Convergent Plug-and-Play Framework with Outliers Detection
Wei, Deliang | Li, Fang | Weng, ShiyangJournal of Scientific Computing, Vol. 96 (2023), Iss. 3
https://doi.org/10.1007/s10915-023-02303-5 [Citations: 1] -
Nonlocal Matrix Rank Minimization Method for Multiplicative Noise Removal
Yan, Hui-Yin
Communications on Applied Mathematics and Computation, Vol. (2024), Iss.
https://doi.org/10.1007/s42967-024-00396-9 [Citations: 0] -
A New Non-Linear Hyperbolic-Parabolic Coupled PDE Model for Image Despeckling
Majee, Sudeb | Ray, Rajendra K. | Majee, Ananta K.IEEE Transactions on Image Processing, Vol. 31 (2022), Iss. P.1963
https://doi.org/10.1109/TIP.2022.3149230 [Citations: 8] -
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
Models for Multiplicative Noise Removal
Feng, Xiangchu | Zhu, Xiaolong2021
https://doi.org/10.1007/978-3-030-03009-4_60-1 [Citations: 1] -
Low-rank constraint with sparse representation for image restoration under multiplicative noise
Chen, Lixia | Zhu, Pingfang | Wang, XuewenSignal, Image and Video Processing, Vol. 13 (2019), Iss. 1 P.179
https://doi.org/10.1007/s11760-018-1344-3 [Citations: 4] -
Single image noise level estimation by artificial noise
Li, Fang | Fang, Famin | Li, Zhi | Zeng, TieyongSignal Processing, Vol. 213 (2023), Iss. P.109215
https://doi.org/10.1016/j.sigpro.2023.109215 [Citations: 1] -
Performance of the Restarted Homotopy Perturbation Method and Split Bregman Method for Multiplicative Noise Removal
Han, Yu Du | Yun, Jae HeonMathematical Problems in Engineering, Vol. 2018 (2018), Iss. P.1
https://doi.org/10.1155/2018/7696798 [Citations: 1] -
A Simplified Convex Optimization Model for Image Restoration with Multiplicative Noise
Che, Haoxiang | Tang, YuchaoJournal of Imaging, Vol. 9 (2023), Iss. 10 P.229
https://doi.org/10.3390/jimaging9100229 [Citations: 0] -
A Convex Variational Approach for Image Deblurring With Multiplicative Structured Noise
Wu, Tingting | Li, Wei | Li, Lihua | Zeng, TieyongIEEE Access, Vol. 8 (2020), Iss. P.37790
https://doi.org/10.1109/ACCESS.2020.2974913 [Citations: 5] -
Multiplicative noise removal with a sparsity-aware optimization model
Lu, Jian | Shen, Lixin | Xu, Chen | Xu, YueshengInverse Problems & Imaging, Vol. 11 (2017), Iss. 6 P.949
https://doi.org/10.3934/ipi.2017044 [Citations: 8] -
Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition
Kong, Xiangyang | Zhao, Yongqiang | Chan, Jonathan Cheung-Wai | Xue, JizeRemote Sensing, Vol. 14 (2022), Iss. 3 P.511
https://doi.org/10.3390/rs14030511 [Citations: 5] -
A new efficient variational model for multiplicative noise removal
Bai, Lufeng | Liu, Fang | Tan, ShenyangInternational Journal of Computer Mathematics, Vol. 97 (2020), Iss. 7 P.1444
https://doi.org/10.1080/00207160.2019.1622688 [Citations: 5] -
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging
Models for Multiplicative Noise Removal
Feng, Xiangchu | Zhu, Xiaolong2023
https://doi.org/10.1007/978-3-030-98661-2_60 [Citations: 1] -
Deep Multi-Level Wavelet-CNN Denoiser Prior for Restoring Blurred Image With Cauchy Noise
Wu, Tingting | Li, Wei | Jia, Shilong | Dong, Yiqiu | Zeng, TieyongIEEE Signal Processing Letters, Vol. 27 (2020), Iss. P.1635
https://doi.org/10.1109/LSP.2020.3023299 [Citations: 25]