Customizable Restoration in Multi-Degradation Scenarios: Joint Deraining and Low-Light Image Enhancement via Perceptual Decoupling
Year: 2024
Type: article
Abstract: 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 inhttps://github.com/lajiaolajiaolajiao/PDLN.
Source: IEEE Access
Author: Zhiyong He
Institution: Nanjing University of Industry Technology
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4405812139