The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
All in One Bad Weather Removal Using Architectural Search
Abstract
Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog, snow and adherent raindrops using a single network. To achieve this, we first design a generator with multiple task-specific encoders, each of which is associated with a particular bad weather degradation type. We utilize a neural architecture search to optimally process the image features extracted from all encoders. Subsequently, to convert degraded image features to clean background features, we introduce a series of tensor-based operations encapsulating the underlying physics principles behind the formation of rain, fog, snow and adherent raindrops. These operations serve as the basic building blocks for our architectural search. Finally, our discriminator simultaneously assesses the correctness and classifies the degradation type of the restored image. We design a novel adversarial learning scheme that only backpropagates the loss of a degradation type to the respective task-specific encoder. Despite being designed to handle different types of bad weather, extensive experiments demonstrate that our method performs competitively to the individual and dedicated state-of-the-art image restoration methods.
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“Experimental results showed that Ren et al. proposed system outperformed all other methods that were benchmarked, including the newer approaches proposed by Quan et al. [53] and Li et al. [76].”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [76]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
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
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
Research works printed but not linked: 3
- liu, 2017, Desnownet: Context-aware deep network for snow removal, CoRR, abs 1708 4512
- liu, 0, DARTS: Differentiable architecture search, International Conference on Learning Representations
- ren, 0, Single image dehazing via multiscale convolutional neural networks, ECCV
Cited by
Sorted by citations
Liu, R., Ma, L., Zhang, J., Fan, X., & Luo, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01042 · Cited by 1,133
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models
Özdenizci, O., & Legenstein, R. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3238179 · Cited by 525
All-In-One Image Restoration for Unknown Corruption
Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., & Peng, X. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01693 · Cited by 467
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
Valanarasu, J. M. J., Yasarla, R., & Patel, V. M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00239 · Cited by 479
Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors
Zhang, K., Li, R., Yu, Y., Luo, W., & Li, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3104166 · Cited by 387
Self-citation
Metadata
- Authors
- Li, R., Tan, R. T., & Cheong, L.-F.
- Affiliations
- National University of Singapore (SG)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3172-3182
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035713416
- Cited by
- 377
- References
- 65
- Citations by year
- 2026: 63 · 2025: 97 · 2024: 93 · 2023: 66 · 2022: 29 · 2021: 24 · 2020: 5
- FWCI
- 9.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- rain removal, fog removal, snow removal, neural architecture search, adversarial learning
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