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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]
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- liu, 0, DARTS: Differentiable architecture search, International Conference on Learning Representations
- ren, 0, Single image dehazing via multiscale convolutional neural networks, ECCV
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Wang, T., Xia, P., Li, B., Jiang, P.-T., Kong, Z., Zhang, K., Lü, T., & Luo, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01269 · Cited by 5
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Xue, X., Meng, X., Ma, L., Wang, Y., Liu, R., & Fan, X. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428351 · Cited by 5
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Collaborative Semantic Contrastive for All-in-one Image Restoration
Hu, B., Yang, S., Liu, F., & Ding, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110017 · Cited by 5
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Fast and Lightweight UAV-based Road Image Enhancement Under Multiple Low-Visibility Conditions
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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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