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关于图像去噪的综述及优化模型的提出
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Abstract:
在数字图像处理领域中，图像去噪一直是一个基础而关键的课题，尤其是随着遥感技术的发展和应用，对高质量图像的需求日益增长。对于图像去噪的方法却是多种多样的，而缺乏系统地对这些方法进行归类和分析。所以本文详细探讨了现代图像去噪技术的进展，我们将图像去噪方法分为这三大类：全变分、稀疏理论、深度学习。并系统地进行展开论述其中的优缺点，通过列举主要参考文献，并根据参考文献来给我们提供思路，并对这些参考文献仔细深入研究。总结这些参考文献的优点对于我们的启发，并提供建立优化模型的理论基础。最后我们将在本文中的各种方法下提供一些具体的参考优化模型、对于一些高光谱图像数据和条纹噪声的属性进行具体分析，为其它学者提供一些新的建立优化模型的灵感。这些参考文献中的研究成果和我们提供给读者的优化模型有助于推动遥感图像处理技术的发展具有重要意义。
In the field of digital image processing, image denoising has always been a basic and key subject, especially with the development and application of remote sensing technology, the demand for high-quality images is increasing day by day. There are a variety of methods for image denoising, but there is a lack of systematic classification and analysis of these methods. Therefore, this paper discusses the progress of modern image denoising technology in detail. We divide image denoising methods into three categories: Total variational, sparse theory and deep learning. And systematically expounded the advantages and disadvantages of them, by listing the main references, and according to the references to provide us with ideas, and these references carefully in-depth study. Summarize the advantages of these references for our inspiration and provide a theoretical basis for building optimization models. Finally, we will provide some specific reference optimization models under the various methods in this paper, and analyze the attributes of some hyperspectral image data and fringe noise in detail, so as to provide some new inspiration for other scholars to build optimization models. The research results in these references and the optimization model we provide to the readers are of great significance to promote the development of remote sensing image processing technology.
| [1] | Rudin, L.I., Osher, S. and Fatemi, E. (1992) Nonlinear Total Variation Based Noise Removal Algorithms. Physica D: Nonlinear Phenomena, 60, 259-268. https://doi.org/10.1016/0167-2789(92)90242-F |
| [2] | Farsiu, S., Robinson, M.D., Elad, M., et al. (2004) Fast and Robust Multiframe Super Resolution. IEEE Transactions on Image Processing, 13, 1327-1344. https://doi.org/10.1109/TIP.2004.834669 |
| [3] | Wang, Y., Yang, J., Yin, W., et al. (2008) A New Alternating Minimization Algorithm for Total Variation Image Reconstruction. SIAM Journal on Imaging Sciences, 1, 248-272. https://doi.org/10.1137/080724265 |
| [4] | Yang, J., Yin, W., Zhang, Y., et al. (2009) A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration. SIAM Journal on Imaging Sciences, 2, 569-592. https://doi.org/10.1137/080730421 |
| [5] | Jon, K., Liu, J., Wang, X., et al. (2021) Weighted Hyper-Laplacian Prior with Overlapping Group Sparsity for Image Restoration under Cauchynoise. Journal of Scientific Computing, 87, 1-32. https://doi.org/10.1007/s10915-021-01461-8 |
| [6] | Cai, W., Jiang, J. and Ouyang, S. (2021) Hyperspectral Image Denoising Using Adaptive Weight Graph Total Variation Regularization and Low-Rank Matrix Recovery. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2021.3113078 |
| [7] | He, W., Zhang, H., Zhang, L., et al. (2015) Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration. IEEE Transactions on Geoscience and Remote Sensing, 54, 178-188. https://doi.org/10.1109/TGRS.2015.2452812 |
| [8] | Cheng, M.H., Huang, T.Z., Zhao, X.L., et al. (2019) A Variational Model with Hybrid Hyper-Laplacian Priors for Retinex. Applied Mathematical Modelling, 66, 305-321. https://doi.org/10.1016/j.apm.2018.09.022 |
| [9] | Aggarwal, H.K. and Majumdar, A. (2016) Hyperspectral Image Denoising Using Spatio-Spectral Total Variation. IEEE Geoscience and Remote Sensing Letters, 13, 442-446. https://doi.org/10.1109/LGRS.2016.2518218 |
| [10] | Chang, Y., Yan, L., Fang, H., et al. (2015) Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping. IEEE Transactions on Image Processing, 24, 1852-1866. https://doi.org/10.1109/TIP.2015.2404782 |
| [11] | Takeyama, S., Ono, S. and Kumazawa, I. (2020) A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization. Remote Sensing, 12, 3541. https://doi.org/10.3390/rs12213541 |
| [12] | Hu, T., Li, W., Liu, N., et al. (2020) Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms. IEEE Transactions on Geoscience and Remote Sensing, 59, 1516-1533. https://doi.org/10.1109/TGRS.2020.2999634 |
| [13] | Wu, X., Qu, H., Zheng, L., et al. (2021) A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint. Remote Sensing, 13, 5126. https://doi.org/10.3390/rs13245126 |
| [14] | Song, Q., Huang, Z., Ni, H., et al. (2022) Remote Sensing Images Destriping with an Enhanced Low-Rank Prior and Total Variation Regulation. Signal, Image and Video Processing, 16, 1895-1903. https://doi.org/10.1007/s11760-022-02149-8 |
| [15] | Chen, Y., He, W., Yokoya, N., et al. (2019) Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition. IEEE Transactions on Cybernetics, 50, 3556-3570. https://doi.org/10.1109/TCYB.2019.2936042 |
| [16] | Kong, X., Zhao, Y., Xue, J., et al. (2020) Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping. Remote Sensing, 12, Article 704. https://doi.org/10.3390/rs12040704 |
| [17] | Yang, F., Chen, X. and Chai, L. (2021) Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation. Remote Sensing, 13, Article 827. https://doi.org/10.3390/rs13040827 |
| [18] | Zhang, H., Cai, J., He, W., et al. (2021) Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19. https://doi.org/10.1109/TGRS.2021.3061148 |
| [19] | Xu, Z., Xing, H., Fang, S., et al. (2021) Double-Weighted Low-Rank Matrix Recovery Based on Rank Estimation. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, 11-17 October 2021, 172-180. https://doi.org/10.1109/ICCVW54120.2021.00024 |
| [20] | Chen, Y., Huang, T.Z., He, W., et al. (2021) Hyperspectral Image Denoising Using Factor Group Sparsity-Regularized Nonconvex Low-Rank Approximation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16. https://doi.org/10.1109/TGRS.2021.3110769 |
| [21] | Cai, J., He, W. and Zhang, H. (2022) Anisotropic Spatial–Spectral Total Variation Regularized Double Low-Rank Approximation for HSI Denoising and Destriping. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-19. https://doi.org/10.1109/TGRS.2022.3202714 |
| [22] | Yin, M., Adam, T., Paramesran, R., et al. (2022) An l0-Overlapping Group Sparse Total Variation for Impulse Noise Image Restoration. Signal Processing: Image Communication, 102, Article ID: 116620. https://doi.org/10.1016/j.image.2021.116620 |
| [23] | Xu, S., Zhang, J. and Zhang, C. (2022) Hyperspectral Image Denoising by Low-Rank Models with Hyper-Laplacian Total Variation Prior. Signal Processing, 201, Article ID: 108733. https://doi.org/10.1016/j.sigpro.2022.108733 |
| [24] | Peng, J., Wang, H., Cao, X., et al. (2022) Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. https://doi.org/10.1109/TGRS.2022.3229012 |
| [25] | Liu, T., Yang, J., Li, B., et al. (2023) Representative Coefficient Total Variation for Efficient Infrared Small Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-18. https://doi.org/10.1109/TGRS.2023.3324821 |
| [26] | Kim, N., Han, S.S. and Jeong, C.S. (2023) ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image. IEEE Access, 11, 106587-106606. https://doi.org/10.1109/ACCESS.2023.3319268 |
| [27] | Wang, Y., Tang, Y.Y., Zou, C., et al. (2017) Spectral-Spatial Hyperspectral Image Destriping Using Sparse Learning and Spatial Unidirection Prior. 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), Exeter, 21-23 June 2017, 1-5. https://doi.org/10.1109/CYBConf.2017.7985812 |
| [28] | Zhuang, L. and Bioucas-Dias, J.M. (2018) Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 730-742. https://doi.org/10.1109/JSTARS.2018.2796570 |
| [29] | Peng, Y., Li, W., Luo, X., et al. (2021) Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition with Hyper-Laplacian Prior. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5453-5469. https://doi.org/10.1109/JSTARS.2021.3076170 |
| [30] | Wan, Y., Ma, A., He, W., et al. (2021) Accurate Multi-Objective Low-Rank and Sparse Model for Hyperspectral Image Denoising Method. IEEE Transactions on Evolutionary Computation, 27, 37-51. |
| [31] | Zhang, Z. and Yang, F. (2022) Hyperspectral Image Denoising and Destriping Based on Sparse Representation, Graph Laplacian Regularization and Stripe Low-Rank Property. EURASIP Journal on Advances in Signal Processing, 2022, Article No. 96. https://doi.org/10.1186/s13634-022-00901-3 |
| [32] | Seghouane, A.K., Iqbal, A. and Rekavandi, A.M. (2023) RBDL: Robust Block-Structured Dictionary Learning for Block Sparse Representation. Pattern Recognition Letters, 172, 89-96. https://doi.org/10.2139/ssrn.4273623 |
| [33] | Yue, J., Yang, C., Li, Y., et al. (2023) A Spectrum Denoising Method Based on Low-Rank Recovery Dictionary Learning with Its Application to Chemical Oxygen Demand Detection. Chemometrics and Intelligent Laboratory Systems, 240, Article ID: 104919. https://doi.org/10.1016/j.chemolab.2023.104919 |
| [34] | Tian, H.M. and Wang, Y. (2024) Optimal Selection of Dictionary Atoms for Sparse Dictionary Learning of Time-Varying Monitoring Data in Two-Dimensional Geotechnical Problems. Computers and Geotechnics, 165, Article ID: 105953. https://doi.org/10.1016/j.compgeo.2023.105953 |
| [35] | Fu, Y., Luo, C., Xu, X., et al. (2024) Graph Embedding Dictionary Pair Learning for Robust Process Monitoring. Measurement, 228, Article ID: 114287. https://doi.org/10.1016/j.measurement.2024.114287 |
| [36] | Xu, B., Jiang, F., Zhu, Z., et al. (2024) Adaptive Convolutional Dictionary Learning for Denoising Seismocardiogram to Enhance the Classification Performance of Aortic Stenosis. Computers in Biology and Medicine, 168, Article ID: 107763. https://doi.org/10.1016/j.compbiomed.2023.107763 |
| [37] | Song, Y.C., Wu, F.Y., Ni, Y.Y., et al. (2023) A Fast Threshold Omp Based on Self-Learning Dictionary for Propeller Signal Reconstruction. Ocean Engineering, 287, Article ID: 115792. https://doi.org/10.1016/j.oceaneng.2023.115792 |
| [38] | Mohseni-Sehdeh, S. and Babaie-Zadeh, M. (2023) A Fast Dictionary-Learning-Based Classification Scheme Using Undercomplete Dictionaries. Signal Processing, 212, Article ID: 109124. https://doi.org/10.1016/j.sigpro.2023.109124 |
| [39] | Yang, M., Ling, J., Chen, J., et al. (2023) Discriminative Semi-Supervised Learning via Deep and Dictionary Representation for Image Classification. Pattern Recognition, 140, Article ID: 109521. https://doi.org/10.1016/j.patcog.2023.109521 |
| [40] | Wang, H., Dong, G., Chen, J., et al. (2023) A Novel Dictionary Learning Named Deep and Shared Dictionary Learning for Fault Diagnosis. Mechanical Systems and Signal Processing, 182, Article ID: 109570. https://doi.org/10.1016/j.ymssp.2022.109570 |
| [41] | Lee, S., Negishi, M., Urakubo, H., et al. (2020) Mu-Net: Multi-Scale U-Net for Two-Photon Microscopy Image Denoising and Restoration. Neural Networks, 125, 92-103. https://doi.org/10.1016/j.neunet.2020.01.026 |
| [42] | Cao, X., Fu, X., Xu, C., et al. (2021) Deep Spatial-Spectral Global Reasoning Network for Hyperspectral Image Denoising. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14. https://doi.org/10.1109/TGRS.2021.3069241 |
| [43] | Huang, J., Zhao, Z., Ren, C., et al. (2022) A Prior-Guided Deep Network for Real Image Denoising and Its Applications. Knowledge-Based Systems, 255, Article ID: 109776. https://doi.org/10.1016/j.knosys.2022.109776 |
| [44] | Sun, Z., Zhang, M., Sun, H., et al. (2023) Multi-Modal Deep Convolutional Dictionary Learning for Image Denoising. Neurocomputing, 562, Article ID: 126918. https://doi.org/10.1016/j.neucom.2023.126918 |
| [45] | Jin, Y., Qin, C., Liu, J., et al. (2024) A Novel Deep Wavelet Convolutional Neural Network for Actual Ecg Signal Denoising. Biomedical Signal Processing and Control, 87, Article ID: 105480. https://doi.org/10.1016/j.bspc.2023.105480 |
| [46] | Torun, O., Yuksel, S.E., Erdem, E., et al. (2024) Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks. Signal Processing, 214, Article ID: 109248. https://doi.org/10.1016/j.sigpro.2023.109248 |
| [47] | Qin, Y., Yang, R., He, B., et al. (2024) Multi-Layer Convolutional Dictionary Learning Network for Signal Denoising and Its Application to Explainable Rolling Bearing Fault Diagnosis. ISA Transactions. https://doi.org/10.1016/j.isatra.2024.01.027 |
| [48] | Shuai, L., Li, Z., Chen, Z., et al. (2024) A Research Review on Deep Learning Combined with Hyperspectral Imaging in Multiscale Agricultural Sensing. Computers and Electronics in Agriculture, 217, Article ID: 108577. https://doi.org/10.1016/j.compag.2023.108577 |
| [49] | Bayoudh, K. (2023) A Survey of Multimodal Hybrid Deep Learning for Computer Vision: Architectures, Applications, Trends, and Challenges. Information Fusion, 105, Article ID: 102217. https://doi.org/10.1016/j.inffus.2023.102217 |
| [50] | Zhang, J., Gong, W., Ye, L., et al. (2024) A Review of Deep Learning Methods for Denoising of Medical Low-Dose CT Images. Computers in Biology and Medicine, 171, Article ID: 108112. https://doi.org/10.1016/j.compbiomed.2024.108112 |
