IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model

2026-05-15 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Researchers from Tsinghua University and NanKai University introduce IG-Diff, an illumination-guided diffusion model designed for complex night scene restoration. This model addresses the challenge of concurrently handling low-light conditions and other degradations like rain, fog, or snow, which existing methods struggle with due to a lack of comprehensive paired training data. The team developed a data synthesis pipeline to create diverse datasets simulating both illumination and various weather degradations. IG-Diff integrates an illumination-guided module within a diffusion model to direct the restoration process, preserving texture fidelity and mitigating issues like over-exposure or under-exposure. Experiments on synthetic and real-world datasets, including LOL-weather, LOL-Blur-Noise, and LOL-v1, demonstrate IG-Diff's superior performance over 12 baseline methods, including other diffusion-based solutions, in restoring complex night images.

Key takeaway

For research scientists developing image restoration algorithms, IG-Diff demonstrates that integrating an illumination-guided module into a diffusion model, coupled with meticulously synthesized multi-degradation datasets, significantly improves performance in complex night scenes. You should consider developing similar data synthesis pipelines and illumination-aware architectures to overcome limitations of single-degradation models and address real-world challenges in surveillance and autonomous driving.

Key insights

Illumination-guided diffusion models effectively restore complex night scenes by integrating illumination priors and synthetic data.

Principles

• Synthesize data for complex, multi-degradation scenarios.
• Illumination priors guide adaptive light restoration.
• Diffusion models excel at intricate texture recovery.

Method

A data synthesis pipeline generates complex night scenes by sequentially applying adverse weather and low-light degradation models. An illumination-guided diffusion model then uses an estimated illumination map via cross-attention to restore images, employing a patch-based strategy for diverse input sizes.

In practice

• Use EC-Zero-DCE in reverse for low-light simulation.
• Apply adverse weather modeling before low-light degradation.
• Employ patch-based restoration with overlapping patches.

Topics

• Illumination-Guided Diffusion
• Night Scene Restoration
• Complex Nighttime Datasets
• Adverse Weather Modeling
• Low-Light Image Enhancement

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.