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

TL;DR

This survey comprehensively reviews traditional, deep learning-based, and hybrid image signal processing techniques for low-illumination image enhancement, highlighting recent advances, challenges, and future research directions.

Contribution

It provides an extensive classification and comparison of existing methods, including their advantages and limitations, offering valuable insights for future developments in low-light image enhancement.

Findings

Deep learning methods outperform traditional techniques in noise reduction.

Hybrid approaches combine strengths of both traditional and deep learning methods.

The survey identifies key challenges and promising future research directions.

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.