Abstract
Rain streaks usually give rise to visual degradation and cause many computer vision algorithms to fail. So it is necessary to develop an effective deraining algorithm as preprocess of high-level vision tasks. In this paper, we propose a novel deep learning based deraining method. Specifically, the multi-scale kernels and feature maps are both important for single image deraining. However, the previous works ignore the two multi-scale information or only consider the multi-scale kernels information. Instead, our method learns multi-scale information both from the perspectives of kernels and feature maps, respectively, by designing spatial contextual information aggregation module and pyramid network module. The former module can capture the rain streaks with different sizes and the latter module can extract rain streaks from different scales further. Moreover, we also employ squeeze-and-excitation and skip connections to enhance the correlation between channels and transmit the information from low-level to high-level, respectively. The experimental results show that the proposed method achieves significant improvements over the recent state-of-the-art methods in Rain100H, Rain100L, Rain1200 and Rain1400 datasets.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Brewer N, Liu N (2008) Using the shape characteristics of rain to identify and remove rain from video. In: Structural, syntactic, and statistical pattern recognition, pp 451–458. https://doi.org/10.1007/978-3-540-89689-0_49
Chen J, Tan C, Hou J, Chau L, Li H (2018) Robust video content alignment and compensation for rain removal in a CNN framework. In: CVPR, pp 6286–6295. https://doi.org/10.1109/CVPR.2018.00658. http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Robust_Video_content_cvpr_2018_paper.html
Chen Y, Hsu C (2013) A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In: ICCV, pp 1968–1975. https://doi.org/10.1109/ICCV.2013.247
Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J (2018) Cascaded pyramid network for multi-person pose estimation. In: CVPR, pp 7103–7112. https://doi.org/10.1109/CVPR.2018.00742. http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Cascaded_Pyramid_Network_CVPR_2018_paper.html
Fu X, Huang J, Ding X, Liao Y, Paisley J (2017) Clearing the skies: a deep network architecture for single-image rain removal 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: CVPR, pp 1715–1723. https://doi.org/10.1109/CVPR.2017.186
Garg K, Nayar SK (2004) Detection and removal of rain from videos. In: CVPR, pp 528–535. https://doi.org/10.1109/CVPR.2004.79
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: CVPR, pp 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
Huang D, Kang L, Yang M, Lin C, Wang Y (2012) Context-aware single image rain removal. In: ICME, pp 164–169. https://doi.org/10.1109/ICME.2012.92
Huynh-Thu Q, Ghanbari M (2008) Scope of validity of psnr in image/video quality assessment. Electron Lett 44(13):800–801
Kang L, Lin C, Fu Y (2012) Automatic single-image-based rain streaks removal via image decomposition 21(4), 1742–1755. https://doi.org/10.1109/TIP.2011.2179057
Kim J, Lee C, Sim J, Kim C (2013) Single-image deraining using an adaptive nonlocal means filter. In: ICIP, pp 914–917. https://doi.org/10.1109/ICIP.2013.6738189
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: CoRR, vol. arXiv:1412.6980
Li G, He X, Zhang W, Chang H, Dong L, Lin L (2018) Non-locally enhanced encoder-decoder network for single image de-raining. In: ACM MM, pp 1056–1064. https://doi.org/10.1145/3240508.3240636
Li X, Wu J, Lin Z, Liu H, Zha H (2018) Recurrent squeeze-and-excitation context aggregation net for single image deraining. In: ECCV, pp 262–277. https://doi.org/10.1007/978-3-030-01234-2_16
Li Y, Tan RT, Guo X, Lu J, Brown MS (2016) Rain streak removal using layer priors. In: CVPR, pp 2736–2744. https://doi.org/10.1109/CVPR.2016.299
Lin T, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ (2017) Feature pyramid networks for object detection. In: CVPR, pp 936–944. https://doi.org/10.1109/CVPR.2017.106
Luo Y, Xu Y, Ji H (2015) Removing rain from a single image via discriminative sparse coding. In: ICCV, pp. 3397–3405. https://doi.org/10.1109/ICCV.2015.388
Ranjan A, Black MJ (2017) Optical flow estimation using a spatial pyramid network. In: CVPR, pp 2720–2729. https://doi.org/10.1109/CVPR.2017.291
Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651. https://doi.org/10.1109/TPAMI.2016.2572683
Tripathi AK, Mukhopadhyay S (2014) Removal of rain from videos: a review. SIViP 8(8):1421–1430. https://doi.org/10.1007/s11760-012-0373-6
Wang C, Wang H, Su Z, Yang Y (2019) Embedding non-local mean in squeeze-and-excitation network for single image deraining. In: ICMEW, pp 264–269. https://doi.org/10.1109/ICMEW.2019.00-76
Wang C, Zhang M, Pan J, Su Z (2019) Single image rain removal via densely connected contextual and semantic correlation net. J Electron Imag 28(3):033018. https://doi.org/10.1117/1.JEI.28.3.033018
Wang C, Zhang M, Su Z, Wu Y, Yao G, Wang H (2019) Learning a multi-level guided residual network for single image deraining. Signal Process Imag Commun 78:206–215. https://doi.org/10.1016/j.image.2019.07.003. http://www.sciencedirect.com/science/article/pii/S0923596519305582
Wang C, Zhang M, Su Z, Yao G, Wang Y, Sun X, Luo X (2019) From coarse to fine: a stage-wise deraining net. IEEE Access 7:84420–84428. https://doi.org/10.1109/ACCESS.2019.2922549
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Processing 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861
Yang W, Tan RT, Feng J, Liu J, Guo Z, Yan S (2017) Deep joint rain detection and removal from a single image. In: CVPR, pp 1685–1694. https://doi.org/10.1109/CVPR.2017.183
Zhang H, Patel VM (2018) Densely connected pyramid dehazing network. In: CVPR, pp 3194–3203. https://doi.org/10.1109/CVPR.2018.00337. http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Densely_Connected_Pyramid_CVPR_2018_paper.html
Zhang H, Patel VM (2018) Density-aware single image de-raining using a multi-stream dense network. In: CVPR, pp 695–704. https://doi.org/10.1109/CVPR.2018.00079. http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Density-Aware_Single_Image_CVPR_2018_paper.html
Zhang H, Sindagi V, Patel VM (2017) Image de-raining using a conditional generative adversarial network. In: CoRR, vol arXiv:1701.05957
Zhang X, Li H, Qi Y, Leow WK, Ng TK (2006) Rain removal in video by combining temporal and chromatic properties. In: ICME, pp 461–464. https://doi.org/10.1109/ICME.2006.262572
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the Natural Science Foundation of China [grant numbers 61572099]; Major National Science and Technology Project of China [grant number 2018ZX04016001-011].
Rights and permissions
About this article
Cite this article
Wang, C., Wu, Y., Cai, Y. et al. Single image deraining via deep pyramid network with spatial contextual information aggregation. Appl Intell 50, 1437–1447 (2020). https://doi.org/10.1007/s10489-019-01567-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-019-01567-5