Customizable Restoration in Multi-Degradation Scenarios: Joint Deraining and Low-Light Image Enhancement via Perceptual Decoupling
In real-world nighttime photography, images often suffer from both low-light conditions and rain-induced degradations simultaneously. Traditional methods typically address these issues separately, which limits their effectiveness when both types of degradation are present. To address this challenge,...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10816618/ |
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| Summary: | In real-world nighttime photography, images often suffer from both low-light conditions and rain-induced degradations simultaneously. Traditional methods typically address these issues separately, which limits their effectiveness when both types of degradation are present. To address this challenge, we propose the Perceptual Decoupling-based Learning Network (PDLN), designed to separate these degradations by leveraging their distinct physical properties. PDLN models rain and low-light priors and integrates them through the Decoupled Perception Transformer (DPformer), enabling a robust representation of these degradations. This approach harnesses interactions in both the spatial and frequency domains, resulting in state-of-the-art performance and allowing for personalized enhancement styles. The code and dataset will be available after receipt in <uri>https://github.com/lajiaolajiaolajiao/PDLN</uri>. |
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| ISSN: | 2169-3536 |