|
|
基于深度图像先验的椒盐噪声图像去噪
|
Abstract:
为了有效地去除图像中的椒盐噪声，本文利用低秩和深度图像先验，提出了一种基于加权核范数的优化模型。为了有效地求解优化模型，本文利用双线性分解，采用交替方向法将原问题分解成几个优化的子问题，对每个子问题给出相应的优化算法。数值实验表明，相比其它先进的方法，假设的新模型取得更好的去噪效果。
To effectively remove salt-and-pepper noise from images, this paper proposes an optimization model based on weighted nuclear norm with the prior knowledge of low rank and depth image prior. To efficiently solve the optimization model, the paper utilizes bilinear decomposition and employs the Alternating Direction Method to decompose the original problem into several optimized sub-problems, for each of which corresponding optimization algorithms are provided. Numerical experiments demonstrate that compared to other advanced methods, the proposed new model achieves better denoising results.
| [1] | 阮秋琦. 数字图像处理学[M]. 北京: 电子工业出版社, 2001. |
| [2] | 蒋刚毅, 黄大江, 王旭, 等. 图像质量评价方法研究进展[J]. 电子与信息学报, 2010, 32(1): 219-226. |
| [3] | 闫友彪, 陈元琰. 机器学习的主要策略综述[J]. 计算机应用研究, 2004, 21(7): 4-10, 13. |
| [4] | Martinez, A.M. and Kak, A.C. (2001) PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 228-233. https://doi.org/10.1109/34.908974 |
| [5] | Candès, E.J., Li, X., Ma, Y. and Wright, J. (2011) Robust Principal Component Analysis? Journal of the ACM, 58, 1-37. https://doi.org/10.1145/1970392.1970395 |
| [6] | Lin, Z., Chen, M. and Ma, Y. (2010) The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. arXiv: 1009.5055. |
| [7] | Chen, M., Ganesh, A., Lin, Z., et al. (2009) Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix. Journal of the Marine Biological Association of the UK, 56, 707-722. |
| [8] | 梁栋, 梁昭, 鲍文霞, 等. 基于非局部正则化稀疏表示的图像去噪算法[J]. 系统工程与电子计术, 2013, 35(5): 1104-1109.4 |
| [9] | 史加荣, 郑秀云, 魏宗田, 等. 低秩矩阵恢复算法综述[J]. 计算机应用研究, 2013, 30(6): 1601-1605. |
| [10] | Gu, S., Zhang, L., Zuo, W. and Feng, X. (2014). Weighted Nuclear Norm Minimization with Application to Image Denoising. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 2862-2869. https://doi.org/10.1109/cvpr.2014.366 |
| [11] | Chang, Y., Yan, L., Fang, H., Zhong, S. and Liao, W. (2019) Hsi-denet: Hyperspectral Image Restoration via Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 57, 667-682. https://doi.org/10.1109/tgrs.2018.2859203 |
| [12] | Zhao, X., Xu, W., Jiang, T., Wang, Y. and Ng, M.K. (2020) Deep Plug-And-Play Prior for Low-Rank Tensor Completion. Neurocomputing, 400, 137-149. https://doi.org/10.1016/j.neucom.2020.03.018 |
| [13] | Ulyanov, D., Vedaldi, A. and Lempitsky, V. (2020) Deep Image Prior. International Journal of Computer Vision, 128, 1867-1888. https://doi.org/10.1007/s11263-020-01303-4 |
| [14] | Chen, L., Jiang, X., Liu, X. and Haardt, M. (2022) Reweighted Low-Rank Factorization with Deep Prior for Image Restoration. IEEE Transactions on Signal Processing, 70, 3514-3529. https://doi.org/10.1109/tsp.2022.3183466 |
| [15] | Cai, J., Candès, E.J. and Shen, Z. (2010) A Singular Value Thresholding Algorithm for Matrix Completion. SIAM Journal on Optimization, 20, 1956-1982. https://doi.org/10.1137/080738970 |
| [16] | Cascarano, P., Sebastiani, A., Comes, M.C., Franchini, G. and Porta, F. (2021). Combining Weighted Total Variation and Deep Image Prior for Natural and Medical Image Restoration via ADMM. 2021 21st International Conference on Computational Science and Its Applications (ICCSA), Cagliari, 13-16 September 2021, 39-46. https://doi.org/10.1109/iccsa54496.2021.00016 |
| [17] | The USC-SIPI Image Database. http://sipi.usc.edu/database/ |
