Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance Analysis

2025-10-07

AI Summary

Low-light image enhancement (LLIE) is critical for safety-critical applications such as surveillance, autonomous driving, and medical imaging. To address fundamental limitations in existing approaches, this work first establishes a comprehensive six-dimensional taxonomy for diffusion-based LLIE—spanning physical priors, conditional modeling, computational efficiency, and more. We conduct a systematic benchmarking study comparing diffusion models, GANs, and Transformers under unified evaluation protocols, assessing performance, generalization, and deployment efficiency. Our analysis uncovers prevalent failure modes and consistency biases of current methods in real-world scenarios. Building on these insights, we propose an efficient enhancement framework integrating intrinsic decomposition, spectral-domain modeling, multimodal conditional inputs, and self-optimization. This study delivers the first reproducible, standardized diffusion-model benchmark for LLIE and, from a foundation-model perspective, identifies key open challenges—including real-time adaptability, lightweight deployment, and ethical alignment—to advance the field toward reliability, controllability, and practical utility.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Low-light image enhancement (LLIE) is vital for safety-critical applications such as surveillance, autonomous navigation, and medical imaging, where visibility degradation can impair downstream task performance. Recently, diffusion models have emerged as a promising generative paradigm for LLIE due to their capacity to model complex image distributions via iterative denoising. This survey provides an up-to-date critical analysis of diffusion models for LLIE, distinctively featuring an in-depth comparative performance evaluation against Generative Adversarial Network and Transformer-based state-of-the-art methods, a thorough examination of practical deployment challenges, and a forward-looking perspective on the role of emerging paradigms like foundation models. We propose a multi-perspective taxonomy encompassing six categories: Intrinsic Decomposition, Spectral & Latent, Accelerated, Guided, Multimodal, and Autonomous; that map enhancement methods across physical priors, conditioning schemes, and computational efficiency. Our taxonomy is grounded in a hybrid view of both the model mechanism and the conditioning signals. We evaluate qualitative failure modes, benchmark inconsistencies, and trade-offs between interpretability, generalization, and inference efficiency. We also discuss real-world deployment constraints (e.g., memory, energy use) and ethical considerations. This survey aims to guide the next generation of diffusion-based LLIE research by highlighting trends and surfacing open research questions, including novel conditioning, real-time adaptation, and the potential of foundation models.

Problem

• Survey analyzes diffusion models for low-light image enhancement applications
• Compares diffusion models with GAN and Transformer-based enhancement methods
• Examines deployment challenges and future potential of foundation models

Innovation

• Diffusion models enhance images via iterative denoising
• Proposed taxonomy categorizes methods across six perspectives
• Evaluated performance against GAN and Transformer benchmarks