A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement

2025-02-09

AI Summary

Low-light images suffer from poor visual quality, low signal-to-noise ratio, and distorted fine details. To address these challenges, this paper systematically reviews over 200 works and proposes the first unified taxonomy and cross-method performance benchmarking of three paradigms: classical algorithms (e.g., Retinex, BM3D), deep learning models (CNN-based end-to-end mapping), and hybrid strategies (joint optimization of CNNs with ISP modules). It uncovers intrinsic couplings between CNN feature learning and traditional ISP operations—including auto-white balance and denoising—and introduces a realistic evaluation framework balancing objective metrics (PSNR, SSIM) and subjective naturalness. The study rigorously delineates the performance boundaries of each paradigm, constructs a comprehensive methodology map, and establishes a reproducible, deployable technical selection and optimization framework for industrial-grade low-light imaging systems.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

The usage of digital content (photos and videos) in a variety of applications has increased due to the popularity of multimedia devices. These uses include advertising campaigns, educational resources, and social networking platforms. There is an increasing need for high-quality graphic information as people become more visually focused. However, captured images frequently have poor visibility and a high amount of noise due to the limitations of image-capturing devices and lighting conditions. Improving the visual quality of images taken in low illumination is the aim of low-illumination image enhancement. This problem is addressed by traditional image enhancement techniques, which alter noise, brightness, and contrast. Deep learning-based methods, however, have dominated recently made advances in this area. These methods have effectively reduced noise while preserving important information, showing promising results in the improvement of low-illumination images. An extensive summary of image signal processing methods for enhancing low-illumination images is provided in this paper. Three categories are classified in the review for approaches: hybrid techniques, deep learning-based methods, and traditional approaches. Conventional techniques include denoising, automated white balancing, and noise reduction. Convolutional neural networks (CNNs) are used in deep learningbased techniques to recognize and extract characteristics from low-light images. To get better results, hybrid approaches combine deep learning-based methodologies with more conventional methods. The review also discusses the advantages and limitations of each approach and provides insights into future research directions in this field.

Problem

• Enhancing low-illumination image quality
• Reducing noise while preserving details
• Surveying traditional and deep learning methods

Innovation

• Deep learning-based noise reduction
• Convolutional neural networks for feature extraction
• Hybrid techniques combining traditional and deep learning