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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Ren et al.'s proposed system outperformed all other benchmarked methods, including Quan et al. [53] and Li et al. [76].
“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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Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
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LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal
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Strip Attention for Image Restoration
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UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework
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Progressive Negative Enhancing Contrastive Learning for Image Dehazing and Beyond
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IDP-YOLOV9: Improvement of Object Detection Model in Severe Weather Scenarios from Drone Perspective
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Image Desnowing via Deep Invertible Separation
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DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
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Weather-aware object detection method for maritime surveillance systems
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Prompt-Based Ingredient-Oriented All-in-One Image Restoration
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Controlling the Rain: from Removal to Rendering
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RobustSAM: Segment Anything Robustly on Degraded Images
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Dual-former: Hybrid self-attention transformer for efficient image restoration
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Omni-Kernel Modulation for Universal Image Restoration
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Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather
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Memory Uncertainty Learning for Real-World Single Image Deraining
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Unified Multi-Weather Visibility Restoration
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SmartAssign:Learning A Smart Knowledge Assignment Strategy for Deraining and Desnowing
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Learning Generalized Segmentation for Foggy-Scenes by Bi-directional Wavelet Guidance
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Exploring the Potential of Pooling Techniques for Universal Image Restoration
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CFMW: Cross-Modality Fusion Mamba for Robust Object Detection Under Adverse Weather
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Context-Aware Pretraining for Efficient Blind Image Decomposition
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Degradation-adaptive neural network for jointly single image dehazing and desnowing
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Learn to Search a Lightweight Architecture for Target-Aware Infrared and Visible Image Fusion
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All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation
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Exploring the Application of Large-Scale Pre-Trained Models on Adverse Weather Removal
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Harmony in Diversity: Improving All-in-One Image Restoration via Multi-Task Collaboration
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Textual prompt guided image restoration
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Progressive Subtractive Recurrent Lightweight Network for Video Deraining
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Dual-frame spatio-temporal feature modulation for video enhancement
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Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models
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Spatial Frequency Modulation Network for Efficient Image Dehazing
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Rain Removal From Light Field Images With 4D Convolution and Multi-Scale Gaussian Process
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Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization GapPreprint
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- 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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