Multiscale image denoising algorithm based on UNet3+
Authors: 
Kui Liu, Yu Liu, Benyue Su, Huiping TangAuthors Info & Claims
Abstract
To fully exploit the multiscale information for image denoising, we introduce the idea of full-scale skip connections in the image segmentation network UNet3+. However, existing UNet3+ networks aggregate multiscale information by directly stitching feature maps, leading to the existence of redundant information. To address this problem, we propose a multiscale selection block for feature selection across multiple convolutional streams as well as within a single scale. Specifically, the selective feature concatenation block dynamically adjusts the receptive field through a self-attentive mechanism to selectively fuse features from multiple resolutions. The dual-stream attention unit performs feature selection from both channel and spatial dimensions within each scale. Additionally, we utilize PixelShuffle for feature reconstruction to enhance multiscale semantic information and maintain information integrity. Based on the above, a novel multiscale image denoising network based on UNet3+ is proposed in this paper. Qualitative and quantitative experimental results show that our network achieves significant results in removing additive white Gaussian noise.
References
[1]
Anwar, S., Barnes, N.: Real image denoising with feature attention. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 3155–3164 (2019).
[2]
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), pp 60–65 vol. 2, (2005).
[3]
Charbonnier, P., Blanc-Feraud, L., Aubert, G., et al.: Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st international conference on image processing, pp 168–172 vol. 2 (1994).
[4]
Charmouti, B., Junoh, A.K., Abdurrazzaq, A., et al.: A new denoising method for removing salt & pepper noise from image. Multimed. Tools Appl. pp 1–13 (2022)
[5]
Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-d transform-domain collaborative filtering IEEE Trans. Image Process. 2007 16 8 2080-2095
[6]
Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th international conference on data mining workshops (ICDMW), pp 241–246 (2016).
[7]
Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 13713–13722 (2021)
[8]
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141 (2018)
[9]
Huang, H., Lin, L., Tong, R., et al.: Unet 3+: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1055–1059 (2020)
[10]
Huang, T., Li, S., Jia, X., et al.: Neighbor2neighbor: Self-supervised denoising from single noisy images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14781–14790 (2021)
[11]
Jiang J, Yang K, Yang J, et al. A new nonlocal means based framework for mixed noise removal Neurocomputing 2021 431 57-68
[12]
Li, X., Wang, W., Hu, X., et al.: Selective kernel networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 510–519 (2019)
[13]
Liu K, Xu W, Wu H, et al. Weighted hybrid order total variation model using structure tensor for image denoising Multimed. Tools Appl. 2023 82 1 927-943
[14]
Liu, P., Zhang, H., Zhang, K., et al.: Multi-level wavelet-cnn for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 773–782 (2018)
[15]
Lyu Z, Zhang C, and Han M DSTnet: a new discrete shearlet transform-based CNN model for image denoising Multimedia Syst. 2021 27 1165-1177
[16]
Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Adv. Neural Inf. Process. Syst. Vol. 29 (2016)
[17]
Parrilli S, Poderico M, Angelino CV, et al. A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage IEEE Trans. Geosci. Remote Sens. 2012 50 2 606-616
[18]
Ronneberger, O., Fischer, P., Brox, T.: 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, Springer, pp 234–241 (2015)
[19]
Sadeghi, F., Levine, S.: Cad2rl: Real single-image flight without a single real image. arXiv preprint arXiv:1611.04201 (2016)
[20]
Singh A, Kushwaha S, Alarfaj M, et al. Comprehensive overview of backpropagation algorithm for digital image denoising Electronics 2022 11 10 1590
[21]
Singh, N.K., Raza, K.: Medical image generation using generative adversarial networks: a review. A computational perspective in healthcare, Health informatics. pp. 77–96. (2021)
[22]
Thakur RK and Maji SK Multi scale pixel attention and feature extraction based neural network for image denoising Pattern Recogn. 2023 141 109603
[23]
Tian C, Fei L, Zheng W, et al. Deep learning on image denoising: An overview Neural Netw. 2020 131 251-275
[24]
Tian C, Xu Y, Li Z, et al. Attention-guided cnn for image denoising Neural Netw. 2020 124 117-129
[25]
Tian C, Xu Y, and Zuo W Image denoising using deep cnn with batch renormalization Neural Netw. 2020 121 461-473
[26]
Tian C, Xu Y, Zuo W, et al. Designing and training of a dual cnn for image denoising Knowl.-Based Syst. 2021 226 106949
[27]
Tian C, Zheng M, Zuo W, et al. Multi-stage image denoising with the wavelet transform Pattern Recogn. 2023 134 109050
[28]
Woo, S., Park, J., Lee, J.Y., et al.: Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19 (2018)
[29]
Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss IEEE Trans. Med. Imaging 2018 37 6 1348-1357
[30]
Zamir SW, Arora A, Khan S, et al. Learning enriched features for fast image restoration and enhancement IEEE Trans. Pattern Anal. Mach. Intell. 2023 45 2 1934-1948
[31]
Zhang K, Zuo W, Chen Y, et al. Beyond a gaussian denoiser: residual learning of deep CNN for image denoising IEEE Trans. Image Process. 2017 26 7 3142-3155
[32]
Zhang K, Zuo W, and Zhang L FFDnet: toward a fast and flexible solution for CNN-based image denoising IEEE Trans. Image Process. 2018 27 9 4608-4622
[33]
Zhao M, Cao G, Huang X, et al. Hybrid transformer-CNN for real image denoising IEEE Signal Process. Lett. 2022 29 1252-1256
[34]
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., et al.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer, pp 3–11 (2018)
Recommendations
A Multi-scale Dilated Residual Convolution Network for Image Denoising
AbstractDue to the excellent performance of deep learning, more and more image denoising methods based on convolutional neural networks (CNN) are proposed, including dilated convolution method and multi-scale convolution method. A fundamental issue is how ...
A Joint Multiscale Algorithm with Auto-adapted Threshold for Image Denoising
IAS '09: Proceedings of the 2009 Fifth International Conference on Information Assurance and Security - Volume 02Curvelet transform is one of the recently developed multiscale transform, which can well deal with the singularity of line and provides optimally sparse representation of images with edges. But now the image denoising based on curvelet transform is ...
Multiscale LMMSE-based image denoising with optimal wavelet selection
In this paper, a wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoising is proposed, and the determination of the optimal wavelet basis with respect to the proposed scheme is also discussed. The ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 27 March 2024
Accepted: 08 February 2024
Received: 20 July 2023
Author Tags
Qualifiers
- Research-article
Funding Sources
- Major Special Science and Technology Project of Anhui Province
- Key Project of Education Natural Science Research of Anhui Province of China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Dec 2024