ECA-MSNet: A Multi-Scale Residual U-Net with Efficient Channel Attention for Real-World Image Denoising
Main
Abdul Fatah Nasrat*
Tuba Çağlikantar
Real‑world photographs contain complex, sensor‑dependent noise that simultaneously obscures subtle high‑frequency textures and broad contextual cues, making denoising a persistent challenge in low‑level vision. The goal of this study is to devise a single, computationally balanced model that removes such heterogeneous noise while faithfully preserving both fine detail and global structure. We introduce ECA‑MSNet, a dual‑branch convolutional architecture designed around this objective. The Residual Detail Estimation Branch reconstructs delicate textures that are most susceptible to corruption, whereas the Multi‑Scale Feature Restoration Branch—a U‑Net enhanced with Attention‑based Multi‑Scale Residual Blocks and lightweight Efficient Channel Attention (ECA)—captures coarse‑to‑fine contextual information. A Dual Residual Fusion Module adaptively merges the two outputs, and a final Refine Block suppresses residual artifacts, yielding the restored image. Extensive experiments on the SIDD and PolyU real‑noise benchmarks validate the effectiveness of the proposed method. ECA‑MSNet achieves 39.41 dB / 0.9109 SSIM on SIDD and 37.76 dB / 0.9574 SSIM on PolyU, outperforming strong baselines such as DnCNN, FFDNet, CBDNet, and CycleISP. Ablation studies further confirm that each architectural component—dual‑branch design, multi‑scale residual blocks, channel attention, and fusion strategy—contributes measurable gains. These results demonstrate that ECA‑MSNet sets a new state of the art for real‑world image denoising, offering a favorable trade‑off between accuracy and efficiency and providing a versatile foundation for other low‑level vision tasks.
Abdelhamed, A. a. (2018). A high-quality denoising dataset for smartphone cameras. Proceedings of the IEEE conference on computer vision and pattern recognition, 1692--1700.
Bae, W. a. (2017). Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 145--153.
Bertalm'o, M. (2018). Denoising of photographic images and video. Fundamentals, open challenges and new trends. Cham, Switzerland: Springer.
Buades, A. a.-M. (2005). A non-local algorithm for image denoising. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), 60--65.
Chen, L. a. (2021). Hinet: Half instance normalization network for image restoration. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 182--192.
Chen, L. a. (2022). Simple baselines for image restoration. European conference on computer vision, 17--33.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, 1251--1258.
Dabov, K. a. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 2080--2095.
Dabov, K. a. (2009). Image denoising with shape-adaptive principal component analysis. Department of Signal Processing, Tampere University of Technology, France.
Fang, F. a. (2020). Multilevel edge features guided network for image denoising. IEEE Transactions on Neural Networks and Learning Systems, 3956--3970.
Foi, A. (2009). Clipped noisy images: Heteroskedastic modeling and practical denoising. Signal Processing, 2609--2629.
Guo, S. a. (2019). Toward convolutional blind denoising of real photographs. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 1712--1722.
Li, Y. a. (2023). NTIRE 2023 challenge on image denoising: Methods and results. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1905--1921.
Liang, J. a. (2021). Swinir: Image restoration using swin transformer. Proceedings of the IEEE/CVF international conference on computer vision, 1833--1844.
Pan, T. a. (2023). Hybrid attention compression network with light graph attention module for remote sensing images. IEEE Geoscience and Remote Sensing Letters, 1--5.
Plotz, T. a. (2017). Benchmarking denoising algorithms with real photographs. Proceedings of the IEEE conference on computer vision and pattern recognition, 1586--1595.
Schmidt, U. a. (2014). Shrinkage fields for effective image restoration. Proceedings of the IEEE conference on computer vision and pattern recognition, 2774--2781.
Simoncelli, E. P. (1996). Noise removal via Bayesian wavelet coring. Proceedings of 3rd IEEE international conference on image processing, 379--382.
Wang, Q. a. (2020). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11534--11542.
Woo, S. a.-Y. (2018). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), 3--19.
Xu, J. a. (2015). Patch group based nonlocal self-similarity prior learning for image denoising. Proceedings of the IEEE international conference on computer vision, 244--252.
Xu, J. a. (2018). Real-world noisy image denoising: A new benchmark. arXiv preprint arXiv:1804.02603.
Yaroslavsky, L. P. (1996). Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window. Wavelet Applications in Signal and Image Processing IV, 2--13.
Yu, F. a. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
Yu, W. a. (2022). Metaformer is actually what you need for vision. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 10819--10829.
Zamir, S. W.-H. (2020). Cycleisp: Real image restoration via improved data synthesis. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2696--2705.
Zamir, S. W.-H. (2020). Learning enriched features for real image restoration and enhancement. Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXV 16, 492--511.
Zamir, S. W.-H. (2021). Multi-stage progressive image restoration. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 14821--14831.
Zamir, S. W.-H. (2022). Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 5728--5739.
Zhang, K. a. (2017). Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 3142--3155.
Zhang, K. a. (2018). FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Transactions on Image Processing, 4608--4622.
Zhang, Y. a. (2018). Image super-resolution using very deep residual channel attention networks. Proceedings of the European conference on computer vision (ECCV), 286--301.
Zhang, Y. a. (2023). Kbnet: Kernel basis network for image restoration. arXiv preprint arXiv:2303.02881.
