The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Single Image Deraining: A Comprehensive Benchmark Analysis
Open accessAbstract
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images.This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on the dataset shed light on the comparisons and limitations of state-of-the-art deraining algorithms, and suggest promising future directions.
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons
Bradley, R. A., & Terry, M. E. (1952) · Biometrika · doi:10.2307/2334029 · Cited by 2,412
Learning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · cited 1×
“Data-driven CNN models: Recently, CNNs have achieved dominant success for image restoration [28, 29] including single image deraining [30, 31].”
BackgroundDomain Adaptive Faster R-CNN for Object Detection in the Wild
Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00352 · Cited by 1,672
Density-Aware Single Image De-raining Using a Multi-stream Dense Network
Zhang, H., & Patel, V. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00079 · Cited by 1,066
Restoring an Image Taken through a Window Covered with Dirt or Rain
Eigen, D., Krishnan, D., & Fergus, R. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.84 · Cited by 454
Studying Very Low Resolution Recognition Using Deep Networks
Wang, Z., Chang, S., Yang, Y., Liu, D., & Huang, T. S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.518 · Cited by 235 · cited 1×
“As pointed out by several recent works [41, 15, 42, 43], the performance of high-level computer vision tasks, such as object detection and recognition, will deteriorate in the presence of various sensory and environmental degradations.”
BackgroundSelf-citation
Cited by
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,715
Self-citationLow-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Self-citationSingle Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges
Ren, W., Pan, J., Zhang, H., Cao, X., & Yang, M.-H. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01235-8 · Cited by 382
Self-citationLet’s Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Uricar, M., Sistu, G., Rashed, H., Vobecky, A., Kumar, V. R., Krizek, P., Burger, F., & Yogamani, S. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00081 · Cited by 66
FHDe2Net: Full High Definition Demoireing Network
He, B., Wang, C., Shi, B., & Duan, L.-Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58542-6_43 · Cited by 62
Metadata
- Authors
- Li, S., Araujo, I. B., Ren, W., Wang, Z., Tokuda, E. K., Junior, R. H., Cesar-Junior, R., Zhang, J., Guo, X., & Cao, X.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3833-3842
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2967584026
- Cited by
- 342
- References
- 9
- FWCI
- 14.67
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, S., Araujo, I. B., Ren, W., Wang, Z., Tokuda, E. K., Junior, R. H., Cesar-Junior, R., Zhang, J., Guo, X., & Cao, X. (2019). Single Image Deraining: A Comprehensive Benchmark Analysis. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00396
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