Abstract
A lot of current work based on convolutional neural networks (CNNs) has fetched good visual results on AWGN (additive white Gaussian noise) removal. However, ordinary neural networks are unable to recover detailed information for complex tasks, and the application of a single Gaussian denoising model is greatly limited. To improve the practicality of the denoising algorithm, we trained a DCNN (deep convolutional neural network) to perform multiple denoising tasks, including Gaussian denoising and blind Gaussian denoising. The proposed CNN denoising model with a residual structure and apply feature attention to exploit channel dependency. The network structure mainly consists of sparse block (SB), feature fusion block (FFB), feature compression block (FCB), information interaction block (IIB) and reconstruction block (RB). The SB with sparse mechanism obtains global and local features by alternating between dilated convolution and common convolution. The FFB collects and fuses global and local features to provide additional information for the latter network. The FCB refines the extracted information and compresses the network. The IIB is used for feature integration and dimensionality reduction. Finally, the RB is used to reconstruct the denoised image. A channel attention mechanism is added to the network, and a trade-off is made between the denoising effect and the complexity of the network. A large number of experiments are conducted on five datasets, and the results showed that the proposed method achieves highly competitive performance in both objective evaluation indicators and subjective visual effects.
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The source code can be accessed at the following URL after the manuscript is published: https://github.com/kldys/FCNet.
References
He W, Zhang H, Zhang L, Shen H (2015) Hyperspectral image denoising via noise-adjusted iterative low-rank matrix approximation. IEEE J Select Top Appl Earth Observ Remote Sens 8(6):3050–3061
Wonga YKe, Zhou Y, Liang YS (2024) Quantum image denoising with machine learning: a novel approach to improve quantum image processing quality and reliability. ar**v preprint ar**v:2402.11645
Sanderson D, Olmos PM, Del Cerro CF, Desco M, Abella M (2024) Diffusion X-ray image denoising. In: Medical imaging with deep learning
Chen M, Pu YF, Bai YC (2021) Low-dose CT image denoising using residual convolutional network with fractional TV loss. Neurocomputing 452:510–520
Holt KM (2014) Total nuclear variation and Jacobian extensions of total variation for vector fields. IEEE Trans Image Process 23(9):3975–3989
Jiang CZ, Wu CM, Lin C, **ao XC (2023) Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root. Complex Intell Syst 9:4213–4226
Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095
Chen Y, Pock T (2016) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256–1272
Fu Bo, Wang Liyan, Luo Zhongxuan (2022) A robust image denoising method with multiview texture-aware convolutional neural networks. IEEE Multimed 29(3):80–90
Wang D, Tang H, Pan J, Tang J (2021) Learning a tree-structured channel-wise refinement network for efficient image deraining. In: 2021 IEEE International Conference on Mand Expo (ICME), pp 1–6
Wang D, Pan J, Tang J (2023) Single image deraining using residual channel attention networks. J Comput Sci Technol 38(2):439–454
Krizhevsky A, Sutskever I, and Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, 25
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778
Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn 62:135–160
Liu L, Chen J, Fieguth P, Zhao G, Chellappa R, Pietikainen M (2018) A survey of recent advances in texture representation. ar**v preprint ar**v:1801.10324, 3
Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448–456
Wu Y, Li Y, Feng S, Huang M (2023) Pansharpening using unsupervised generative adversarial networks with recursive mixed-scale feature fusion. IEEE J Select Top Appl Earth Observ Remote Sens 16:3742–3759
Guo S, Yan Z, Zhang K, Zuo W, Zhang L (2019) Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1712–1722
Lin C, Qiu CH, Jiang HY, Zou LL (2023) A deep neural network based on prior driven and structural-preserving for SAR image despeckling. IEEE J Select Top Appl Earth Observ Remote Sens 16:6372–6392. https://doi.org/10.1109/JSTARS.2023.3292325
Zhang K, Zuo W, Gu S, Zhang L (2017) Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3929–3938
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. Adv Neural Inf Process Syst, 27
Chen J, Chen J, Chao H, Yang M (2018) Image blind denoising with generative adversarial network based noise modeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3155–3164
Dong W, Wang P, Yin W, Shi G, Wu F, Lu X (2018) Denoising prior driven deep neural network for image restoration. IEEE Trans Pattern Anal Mach Intell 41(10):2305–2318
Liu D, Wen B, Fan Y, Loy CC, Huang TS (2018) Non-local recurrent network for image restoration. Adv Neural Inf Process Syst, 31
Malvar H, He L, and Cutler R (2004, May) High-quality linear interpolation for demosaicing of Bayer-patterned color images. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 3, pp 474–485. IEEE
Yang J, Zhang Weijia, Liu Jiaxing, **zhao Wu, Yang Jie (2022) Generating de-identification facial images based on the attention models and adversarial examples. Alex Eng J 61(11):8417–8429
Yang J, Qiao S, Wang Z, Zuo Z (2023) Adversarial Secret-Identity Generation Model for Face Anonymization in the Internet of Vehicles. IEEE Syst J 7(14):5161–5170
**ao T, Xu Y, Yang K, Zhang J, Peng Y, Zhang Z (2015) The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 842–850
Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, et al (2017) Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3156–3164
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, pp 2048-2057
Anwar S, Barnes N (2019) Real image denoising with feature attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 3155–3164
Tian C, Xu Y, Li Z, Zuo W, Fei L, Liu H (2020) Attention-guided CNN for image denoising. Neural Netw 124:117–129
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. ar** for convolutional neural networks
Luo W, Li Y, Urtasun R, Zemel R (2016) Understanding the effective receptive field in deep convolutional neural networks. Adv Neural Inf Process Syst, 29
Cheng W, Lu J, Zhu X, Hong J, Liu X, Li M, Li P (2019) Dilated residual learning with skip connections for real-time denoising of laser speckle imaging of blood flow in a log-transformed domain. IEEE Trans Med Imaging 39(5):1582–1593
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026–1034
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132–7141
Srivastava R, Greff K, Schmidhuber J (2015) Training very deep networks. Adv Neural Inf Process Syst, 28
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp 234–241. Springer International Publishing
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700–4708
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9
Ephraim Y, Malah D (1985) Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans Acoust Speech Signal Process 33(2):443–445
Gu S, Zhang L, Zuo W, Feng X (2014) Weighted nuclear norm minimization with application to image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2862–2869
Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image restoration. In: 2011 international Conference on Computer Vision, pp 479–486. IEEE
Zhang K, Zuo W, Zhang L (2018) FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans Image Process 27(9):4608–4622
Tian C, Xu Y, Fei L, Wang J, Wen J, Luo N (2019) Enhanced CNN for image denoising. CAAI Trans Intell Technol 4(1):17–23
Wang Y, Chang D, Zhao Y (2021) A new blind image denoising method based on asymmetric generative adversarial network. IET Image Proc 15(6):1260–1272
Xu J, Deng X, Xu M (2022) Revisiting convolutional sparse coding for image denoising: from a multi-scale perspective. IEEE Signal Process Lett 29:1202–1206
Vaksman G, Elad M, Milanfar P (2020) Lidia: lightweight learned image denoising with instance adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp 1–15
Lefkimmiatis Stamatios (2018) Universal denoising networks: a novel CNN architecture for image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8
Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision, vol 2, pp 416–423
Ma K, Duanmu Z, Wu Q, Wang Z, Yong H, Li H, Zhang L (2016) Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans Image Process 26(2):1004–1016
Huang J, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp 5197–5206, https://doi.org/10.1109/CVPR.2015.7299156
Funding
This work is supported by the Hainan Provincial Natural Science Foundation of China (621MS019), Major Science and Technology Project of Haikou (Grant: 2020-009), Innovative Research Project of Postgraduates in Hainan Province (Qhyb2021-10), National Natural Science Foundation of China(Grant: 82260362)).
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SLF and CL wrote the main manuscript text. All authors reviewed the manuscript. ZSQ and GR Zhang participated in the design of the model and experimental analysis, while MXH provided financial support. All authors reviewed and modified the manuscript.
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Feng, S., Qi, Z., Zhang, G. et al. FCNet: a deep neural network based on multi-channel feature cascading for image denoising. J Supercomput 80, 17042–17067 (2024). https://doi.org/10.1007/s11227-024-06045-5
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DOI: https://doi.org/10.1007/s11227-024-06045-5