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一种注意力机制引导的双卷积神经网络图像去噪算法
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Abstract:
近年来深度卷积神经网络在图像去噪中的应用引起了越来越多的研究兴趣。然而，对于复杂的任务，如真实的噪声图像，普通网络无法恢复精细的细节。提出了一种经过注意力机制引导的双重去噪网络来恢复干净的图像。具体来说，该网络由四个模块组成，扩张特征提取块Dilated Feature Extraction Block (DFEB)、动态卷积块Dynamic convolution structure diagram、注意力模块，重建模块。具有稀疏机制的特征提取模块经由两个子网络提取全局和局部特征。增强块收集并融合全局和局部特征，为后者的网络提供补充信息。压缩块细化所提取的信息并压缩网络。最后，利用重建区块重建去噪影像。该网络具有以下优点：1) 双网络结构具有稀疏机制，可以提取不同的特征，增强去噪器的泛化能力。2) 融合全局和局部特征可以提取显著特征，从而恢复复杂噪声图像的细节。大量的实验结果表明，该网络有较好的去噪效果。
In recent years, the application of deep convolutional neural networks in image denoising has attracted more and more research interest. However, for complex tasks, such as real noisy images, ordinary networks cannot recover fine details. A dual denoising network guided by attention mechanism is proposed to restore clean images. Specifically, the network consists of four modules: Dilated Feature Extraction Block (DFEB), Dynamic convolution structure diagram, attention module and reconstruction module. Feature extraction blocks with sparse mechanism extract global and local features through two subnetworks. Enhancement blocks collect and fuse global and local features to provide supplementary information to the latter’s network. The compressed block refines the extracted information and compresses the network. Finally, the reconstructed block is used to reconstruct the denoised image. The network has the following advantages: 1) the dual network structure has a sparse mechanism, which can extract different features and enhance the generalization ability of the noise reducer. 2) Fusion of global and local features can extract significant features to recover the details of complex noise images. A large number of experimental results show that the network has a good noise reduction effect.
| [1] | Fan, L., Zhang, F., Fan, H. and Zhang, C. (2019) Brief Review of Image Denoising Techniques. Visual Computing for Industry, Biomedicine, and Art, 2, Article No. 7. https://doi.org/10.1186/s42492-019-0016-7 |
| [2] | Liu, B. and Liu, J. (2019) Overview of Image Denoising Based on Deep Learning. Journal of Physics: Conference Series, 1176, Article ID: 022010. https://doi.org/10.1088/1742-6596/1176/2/022010 |
| [3] | Zhang, K., Zuo, W., Chen, Y., Meng, D. and Zhang, L. (2017) Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Transactions on Image Processing, 26, 3142-3155. https://doi.org/10.1109/tip.2017.2662206 |
| [4] | He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 770-778. https://doi.org/10.1109/cvpr.2016.90 |
| [5] | Tian, C., Xu, Y. and Zuo, W. (2020) Image Denoising Using Deep CNN with Batch Renormalization. Neural Networks, 121, 461-473. https://doi.org/10.1016/j.neunet.2019.08.022 |
| [6] | Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L. and Liu, H. (2020) Attention-guided CNN for Image Denoising. Neural Networks, 124, 117-129. https://doi.org/10.1016/j.neunet.2019.12.024 |
| [7] | Yu, F. (2015) Multi-Scale Context Aggregation by Dilated Convolutions. |
| [8] | Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L. and Liu, Z. (2020) Dynamic Convolution: Attention over Convolution Kernels. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 14-19 June 2020, 11030-11039. https://doi.org/10.1109/cvpr42600.2020.01104 |
| [9] | Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., et al. (2017) Waterloo Exploration Database: New Challenges for Image Quality Assessment Models. IEEE Transactions on Image Processing, 26, 1004-1016. https://doi.org/10.1109/tip.2016.2631888 |
| [10] | Martin, D., Fowlkes, C., Tal, D. and Malik, J. (2001) A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV, Vol. 2, 416-423. https://doi.org/10.1109/iccv.2001.937655 |
| [11] | Roth, S. and Black, M.J. (2009) Fields of Experts. International Journal of Computer Vision, 82, 205-229. https://doi.org/10.1007/s11263-008-0197-6 |
| [12] | Mairal, J., Bach, F., Ponce, J., Sapiro, G. and Zisserman, A. (2009) Non-Local Sparse Models for Image Restoration. 2009 IEEE 12th International Conference on Computer Vision, Kyoto, 29 September-2 October 2009, 2272-2279. https://doi.org/10.1109/iccv.2009.5459452 |
| [13] | Franzen, R. (1999) Kodak Lossless True Color Image Suite. |
| [14] | Dabov, K., Foi, A., Katkovnik, V. and Egiazarian, K. (2007) Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Transactions on Image Processing, 16, 2080-2095. https://doi.org/10.1109/tip.2007.901238 |
| [15] | 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 |
| [16] | Zoran, D. and Weiss, Y. (2011) From Learning Models of Natural Image Patches to Whole Image Restoration. 2011 International Conference on Computer Vision, Barcelona, 6-13 November 2011, 479-486. https://doi.org/10.1109/iccv.2011.6126278 |
| [17] | Burger, H.C., Schuler, C.J. and Harmeling, S. (2012) Image Denoising: Can Plain Neural Networks Compete with BM3D? 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, 16-21 June 2012, 2392-2399. https://doi.org/10.1109/cvpr.2012.6247952 |
| [18] | Schmidt, U. and Roth, S. (2014) Shrinkage Fields for Effective Image Restoration. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 2774-2781. https://doi.org/10.1109/cvpr.2014.349 |
| [19] | Chen, Y. and Pock, T. (2017) Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1256-1272. https://doi.org/10.1109/tpami.2016.2596743 |
| [20] | Zhang, K., Zuo, W. and Zhang, L. (2018) Ffdnet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising. IEEE Transactions on Image Processing, 27, 4608-4622. https://doi.org/10.1109/tip.2018.2839891 |
| [21] | Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J. and Luo, N. (2019) Enhanced CNN for Image Denoising. CAAI Transactions on Intelligence Technology, 4, 17-23. https://doi.org/10.1049/trit.2018.1054 |
| [22] | Mao, X., Shen, C. and Yang, Y.B. (2016) Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, 5-10 December 2016, 2810-2818. |
| [23] | Tai, Y., Yang, J., Liu, X. and Xu, C. (2017) Memnet: A Persistent Memory Network for Image Restoration. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 4539-4547. https://doi.org/10.1109/iccv.2017.486 |
| [24] | Zhang, L., Li, Y., Wang, P., Wei, W., Xu, S. and Zhang, Y. (2019) A Separation-Aggregation Network for Image Denoising. Applied Soft Computing, 83, Article ID: 105603. https://doi.org/10.1016/j.asoc.2019.105603 |
| [25] | Aljadaany, R., Pal, D.K. and Savvides, M. (2019) Proximal Splitting Networks for Image Restoration. In: Karray, F., et al., Eds., Image Analysis and Recognition, Springer International Publishing, 3-17. https://doi.org/10.1007/978-3-030-27202-9_1 |
| [26] | Tian, C., Xu, Y., Zuo, W., Du, B., Lin, C. and Zhang, D. (2021) Designing and Training of a Dual CNN for Image Denoising. Knowledge-Based Systems, 226, Article ID: 106949. https://doi.org/10.1016/j.knosys.2021.106949 |
