The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
Open accessAbstract
We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.
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“Figure 15 shows one example of a de-raining result using different low-pass filters: guided filtering [24], bilateral filtering [25] and rolling guidance filtering [26].”
BackgroundMethodInfluentialAccurate Image Super-Resolution Using Very Deep Convolutional Networks
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Cited by
Pre-Trained Image Processing Transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01212 · Cited by 2,308
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
Attentive Generative Adversarial Network for Raindrop Removal from A Single Image
Qian, R., Tan, R. T., Yang, W., Su, J., & Liu, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00263 · Cited by 821
Zang, S., Ding, M., Smith, D., Tyler, P., Rakotoarivelo, T., & Kaafar, M. A. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2892497 · Cited by 457
Classification-Driven Dynamic Image Enhancement
Sharma, V., Diba, A., Neven, D., Brown, M. S., Gool, L. V., & Stiefelhagen, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00424 · Cited by 66
Metadata
- Authors
- Fu, X., Huang, J., Ding, X., Liao, Y., & Paisley, J.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-06
- Volume
- 26
- Issue
- 6
- Pages
- 2944-2956
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2509784253
- Cited by
- 940
- References
- 34
- FWCI
- 31.11
- Type
- journal article
- Language
- en
- Open access
- green
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