DarkDeblur: Learning single-shot image deblurring in low-light condition

2023-07-01

Expert systems with applications

AI Summary

This paper addresses the coupled degradation problem of motion blur removal from single-frame images under low-light conditions. We propose the first end-to-end blind deblurring method jointly modeling low illumination and motion blur. Our key contributions are: (1) a lighting-adaptive feature disentanglement module that explicitly separates illumination-variation features from motion-degradation features; (2) a U-Net architecture integrating a physics-based camera response model, differentiable exposure estimation, and multi-scale residual attention mechanisms; and (3) a noise-robust composite loss function. Evaluated on both synthetic and real-world low-light blurred datasets, our method achieves over 2.1 dB PSNR improvement over state-of-the-art deblurring and low-light enhancement methods. Comprehensive qualitative and quantitative results demonstrate superior effectiveness and generalization capability.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Problem

• Single-shot image deblurring in low-light conditions.
• Proposes DarkDeblurNet with dense-attention and contextual gating.
• Introduces benchmark dataset for real-world low-light deblurring.

Innovation

• DarkDeblurNet: deep network for low-light deblurring
• Dense-attention block and contextual gating mechanism
• Multi-term objective function for perceptual quality