Abstract
Image denoising, a critical process in computer vision, aims to restore high-quality images from their noisy counterparts. Significant progress in this field has been made possible by the emergence of various effective deep learning models. However, these methods are typically confined to processing within the single-domain and exhibit weak performance in preserving detailed information, hindering their practical application. To address this issue, we propose an efficient Dual-Domain Information Fusion Network (DIFNet) for image denoising. Specifically, we design an aggregate frequency domain and spatial domain network to capture and fuse the detailed information. The DIFNet employs a Dual-Domain Feature Fusion Module (DFFM) to integrate the extracted dual-domain information, facilitating the recalibration of weights between the spatial and frequency domains, thereby emphasizing and restoring detailed information. In the DFFM, frequency domain information is extracted through a Frequency Domain Attention Module (FDAM), while spatial domain information is acquired via the convolutional blocks in the image denoising baseline model NAFNet. Experimental results demonstrate that the dual-domain denoising method can recover more detail while maintaining denoising performance. Furthermore, the proposed method outperforms state-of-the-art approaches on widely used benchmarks, highlighting its superior performance.
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References
Abdelhamed, A., Lin, S., Brown, M.S.: A high-quality denoising dataset for smartphone cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1692–1700 (2018)
Anwar, S., Barnes, N.: Real image denoising with feature attention. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3155–3164 (2019)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2010)
Cai, Y., Hu, X., Wang, H., Zhang, Y., Pfister, H., Wei, D.: Learning to generate realistic noisy images via pixel-level noise-aware adversarial training. Adv. Neural. Inf. Process. Syst. 34, 3259–3270 (2021)
Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: European Conference on Computer Vision, pp. 17–33. Springer (2022)
Chen, L., Lu, X., Zhang, J., Chu, X., Chen, C.: HINet: half instance normalization network for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 182–192 (2021)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Franzen, R.: Kodak lossless true color image suite. Source 4(2), 9 (1999). http://www.r0kus/graphics/kodak
Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1712–1722 (2019)
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1833–1844 (2021)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)
Ma, K., et al.: Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans. Image Process. 26(2), 1004–1016 (2016)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: 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, ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)
Patil, J., Jadhav, S.: A comparative study of image denoising techniques. Int. J. Innov. Res. Sci. Eng. Technol. 2(3), 787–794 (2013)
Peng, Y., Zhang, L., Liu, S., Wu, X., Zhang, Y., Wang, X.: Dilated residual networks with symmetric skip connection for image denoising. Neurocomputing 345, 67–76 (2019)
Sheng, Z., Liu, X., Cao, S.Y., Shen, H.L., Zhang, H.: Frequency-domain deep guided image denoising. IEEE Trans. Multimed. (2022)
Tian, C., Xu, Y., Zuo, W.: Image denoising using deep CNN with batch renormalization. Neural Netw. 121, 461–473 (2020)
Tian, C., Zheng, M., Zuo, W., Zhang, S., Zhang, Y., Lin, C.W.: A cross transformer for image denoising. Inf. Fusion 102, 102043 (2024)
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114–125 (2017)
Tu, Z., et al.: Maxim: multi-axis MLP for image processing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5769–5780 (2022)
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general U-shaped transformer for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17683–17693 (2022)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Xue, S., Qiu, W., Liu, F., Jin, X.: Wavelet-based residual attention network for image super-resolution. Neurocomputing 382, 116–126 (2020)
Yaroslavsky, L.P.: Digital Picture Processing: An Introduction, vol. 9. Springer Science & Business Media (2012)
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728–5739 (2022)
Zamir, S.W., et al.: Learning enriched features for real image restoration and enhancement. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16, pp. 492–511. Springer (2020)
Zhang, K., Li, Y., Zuo, W., Zhang, L., Van Gool, L., Timofte, R.: Plug-and-play image restoration with deep denoiser prior. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6360–6376 (2021)
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)
Zhang, L., Lukac, R., Wu, X., Zhang, D.: PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras. IEEE Trans. Image Process. 18(4), 797–812 (2009)
Zhang, L., Wu, X., Buades, A., Li, X.: Color demosaicking by local directional interpolation and nonlocal adaptive thresholding. J. Electron. Imaging 20(2), 023016–023016 (2011)
Zhuge, R., Wang, J., Xu, Z., Xu, Y.: Single image denoising with a feature-enhanced network. Neural Netw. 168, 313–325 (2023)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 62176217) and the Innovation Team Funds of China West Normal University (Grant No. KCXTD2022-3). The Chinese Government Guidance Fund on Local Science and Technology Development of Sichuan Province (24ZYRGZN0018).
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Wu, Z. et al. (2025). DIFNet: Dual-Domain Information Fusion Network for Image Denoising. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15038. Springer, Singapore. https://doi.org/10.1007/978-981-97-8685-5_20
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DOI: https://doi.org/10.1007/978-981-97-8685-5_20
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