Abstract
The performance of vision systems can be affected when used in severe weather conditions such as heavy rain or snow. Rain streak removal is an ill posed problem as they can vary in shape, size, and density across the image. In this paper, a single-image deraining network named Factorized Multi-scale Multi-resolution Residual Network (FMMRNet), which follows a U-Net backbone, is proposed. As rain streaks affect non-local regions of the image, larger receptive fields are beneficial to capture these non-local dependencies. We propose the use of multi-scale grouped convolutions to integrate information from global and local scales. In the proposed FMMRNet, multi-scale convolutions are factorized so that they can have large effective kernel sizes while reducing the computational complexity. During training, intermediate multi-resolution outputs are produced for loss computation which improves gradient flow in the deeper layers of the network and promotes better learning. A channel-wise attention mechanism is included to recalibrate feature maps before fusion instead of direct concatenation at each stage of the decoder. A higher-level reconstruction loss called perceptual loss is included for effective training to improve the visual quality of the derained images. The performance of the proposed FMMRNet is quantitatively and qualitatively compared on public benchmark datasets, and it outperforms the state-of-the-art deraining methods. Furthermore, to show the practical applicability of the proposed network, we demonstrate that when FMMRNet is used as a preprocessing step for object-detection methods such as Faster-RCNN and YOLO, it improves their performance on images degraded by rain streaks by 12.6% and 75.2% respectively.
Similar content being viewed by others
Code Availability
The code for this project will be made available.
References
Cai L, Li SY, Ren D, Wang P (2019) Dual recursive network for fast image deraining. In: 2019 IEEE international conference on image processing (ICIP), pp 2756–2760. https://doi.org/10.1109/ICIP.2019.8803308
Everingham M, Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2). https://doi.org/10.1007/s11263-009-0275-4
Fan W, Wu Y, Wang C (2021) Single image rain streak removal via layer similarity prior. Appl Intell. https://doi.org/10.1007/s10489-020-02056-w
Fan Z, Wu H, Fu X, Huang Y, Ding X (2018) Residual-guide network for single image deraining. In: 2018 ACM multimedia conference on multimedia conference - MM ’18, pp. 1751–1759. ACM Press. https://doi.org/10.1145/3240508.3240694. http://dl.acm.org/citation.cfm?doid=3240508.3240694
Fu X, Huang J, Ding X, Liao Y, Paisley J (2017) Clearing the skies: A deep network architecture for single-image rain removal. IEEE Trans Image Process 26(6):2944–2956. https://doi.org/10.1109/TIP.2017.2691802
Fu X, Huang J, Zeng D, Huang Y, Ding X, Paisley J (2017) Removing rain from single images via a deep detail network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1715–1723. https://doi.org/10.1109/CVPR.2017.186. http://ieeexplore.ieee.org/document/8099669/
Gou Y, Li B, Liu Z, Yang S, Peng X (2020) Clearer: Multi-scale neural architecture search for image restoration. In: Larochelle H., Ranzato M., Hadsell R., Balcan M.F. , Lin H. (eds) Advances in neural information processing systems. https://proceedings.neurips.cc/paper/2020/file/c6e81542b125c36346d9167691b8bd09-Paper.pdf, vol 33. Curran Associates, Inc., pp 17129–17140
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, pp 770–778
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
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, pp 694–711
Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. International Conference on Learning Representations. arXiv:1412.6980
Li B, Gou Y, Gu S, Liu JZ, Zhou JT, Peng X (2021) You only look yourself: Unsupervised and untrained single image dehazing neural network. Int J Comput Vis 129(5):1754– 1767
Li P, Tian J, Tang Y, Wang G, Wu C (2020) Model-based deep network for single image deraining. IEEE Access 8:14036–14047. https://doi.org/10.1109/ACCESS.2020.2965545
Li R, Cheong LF, Tan RT (2019) Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, pp. 1633–1642. https://doi.org/10.1109/CVPR.2019.00173. https://ieeexplore.ieee.org/document/8953352/
Li X, Wu J, Lin Z, Liu H, Zha H (2018) Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: Computer vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11211. Springer, Cham, pp 254–269
Li Y, Tan RT, Guo X, Lu J, Brown MS (2017) Single image rain streak decomposition using layer priors. IEEE Trans Image Process 26(8):3874–3885. https://doi.org/10.1109/TIP.2017.2708841
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P., Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision. Springer, pp 740–755
Luo Y, Zhu J, Ling J, Wu E (2018) Fast removal of rain streaks from a single image via a shape prior. IEEE Access 6:60069–60078. https://doi.org/10.1109/ACCESS.2018.2875171
Odena A, Dumoulin V, Olah C (2016) Deconvolution and checkerboard artifacts. Distill. https://doi.org/10.23915/distill.00003. http://distill.pub/2016/deconv-checkerboard
Qian R, Tan RT, Yang W, Su J, Liu J (2018) Attentive generative adversarial network for raindrop removal from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2482–2491
Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv:1804.02767
Ren D, Zuo W, Hu Q, Zhu P, Meng D (2019) Progressive image deraining networks: A better and simpler baseline. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, pp 3932–3941. https://doi.org/10.1109/CVPR.2019.00406. https://ieeexplore.ieee.org/document/8953349/
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Cortes C., Lawrence N., Lee D., Sugiyama M., Garnett R. (eds) Advances in neural information processing systems. https://proceedings.neurips.cc/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf, vol 28. Curran Associates, Inc.
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
Wang C, Fan W, Zhu H, Su Z (2020) Single image deraining via nonlocal squeeze-and-excitation enhancing network. Appl Intell 50(9):2932–2944. https://doi.org/10.1007/s10489-020-01693-5
Wang T, Yang X, Xu K, Chen S, Zhang Q, Lau RW (2019) Spatial attentive single-image deraining with a high quality real rain dataset. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, pp. 12262–12271. https://doi.org/10.1109/CVPR.2019.01255. https://ieeexplore.ieee.org/document/8953571/
Wei Y, Zhang Z, Wang Y, Xu M, Yang Y, Yan S, Wang M (2021) Deraincyclegan: Rain attentive cyclegan for single image deraining and rainmaking. IEEE Trans Image Process 30:4788–4801
Wei Y, Zhang Z, Wang Y, Zhang H, Zhao M, Xu M, Wang M (2021) Semi-deraingan: A new semi-supervised single image deraining. In: 2021 IEEE international conference on multimedia and expo (ICME). IEEE, pp. 1–6
Wei Y, Zhang Z, Zhang H, Hong R, Wang M (2019) A coarse-to-fine multi-stream hybrid deraining network for single image deraining. In: 2019 IEEE international conference on data mining (ICDM), pp 628–637. https://doi.org/10.1109/ICDM.2019.00073
Yang W, Tan RT, Feng J, Liu J, Guo Z, Yan S (2017) Deep joint rain detection and removal from a single image. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp. 1685–1694. https://doi.org/10.1109/CVPR.2017.183. http://ieeexplore.ieee.org/document/8099666/
Yasarla R, Patel VM (2020) Confidence measure guided single image de-raining. IEEE Trans Image Process 29:4544–4555. https://doi.org/10.1109/TIP.2020.2973802
Zhang H, Patel VM (2018) Density-aware single image de-raining using a multi-stream dense network. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 695–704. https://doi.org/10.1109/CVPR.2018.00079
Zhang H, Sindagi V, Patel VM (2020) Image de-raining using a conditional generative adversarial network. IEEE Trans Circuits Syst Video Technol 30 (11):3943–3956. https://doi.org/10.1109/TCSVT.2019.2920407
Zhang X, Wang T, Wang J, Tang G, Zhao L (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vision Image Underst 197–198:103003. https://doi.org/10.1016/j.cviu.2020.103003. https://www.sciencedirect.com/science/article/pii/S1077314220300709
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sujit, S., Deivalakshmi S & Ko, SB. Factorized multi-scale multi-resolution residual network for single image deraining. Appl Intell 52, 7582–7598 (2022). https://doi.org/10.1007/s10489-021-02772-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-02772-x