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Degradation-Aware Prompt Learning With Cross-Modal Compensation for Adverse Weather Removal
Summary
This study introduces the Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net) for robust image restoration under adverse weather. DCMPC-Net effectively enhances computer vision reliability by leveraging cross-modal cues for superior degradation modeling and feature compensation.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Adverse weather conditions introduce complex image degradations, significantly impacting computer vision system reliability.
- Current all-in-one restoration models often fail to explicitly model degradation characteristics, limiting their adaptability to diverse weather scenarios.
Purpose of the Study:
- To develop a novel network, DCMPC-Net, that enhances image restoration by explicitly modeling degradation characteristics using cross-modal information.
- To improve the adaptability and performance of image restoration models across various adverse weather conditions.
Main Methods:
- Proposes the Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net) utilizing a unified backbone.
- Introduces Cross-Modal Prompt Generator (CMPG) for creating degradation-aware prompts by integrating textual and visual features.
- Employs Prompt-Guided Attention Alignment Module (PGAAM) for adaptive semantic alignment and Dual Feature Compensation Module (DFCM) for structural fidelity enhancement.
Main Results:
- DCMPC-Net demonstrates robust and perceptually consistent restoration across diverse weather conditions.
- Achieves superior accuracy and visual fidelity compared to state-of-the-art methods in both task-specific and unified restoration settings.
- Code availability facilitates further research and application.
Conclusions:
- DCMPC-Net effectively addresses limitations of existing models by integrating cross-modal semantic guidance with spatial alignment and structural enhancement.
- The proposed method significantly improves the reliability and performance of computer vision systems facing adverse weather degradations.
- DCMPC-Net represents a significant advancement in unified image restoration for challenging environmental conditions.
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