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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Sri Rahayu
Program Studi Informatika, Fakultas Teknik, Universitas Majalengka, Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Analysis of Trends and Development of Low-Light Image Enhancement Methods in Computer Vision Ani Sanirah; Sri Rahayu; Ade Bastian
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2372

Abstract

This study investigates the development of Low-Light Image Enhancement (LLIE) methods in the field of computer vision using a Systematic Literature Review (SLR) approach. The review was conducted on 56 scientific articles selected from a total of 604 papers entirely sourced from the Scopus database based on the PRISMA 2020 guidelines. The results indicate that LLIE research has evolved from traditional methods, such as histogram equalization and Retinex, toward deep learning-based approaches including CNN, GAN, Transformer, and diffusion models. Modern methods have demonstrated superior performance in improving image illumination, preserving details, and reducing noise. In addition, real-world datasets and zero-reference approaches are increasingly adopted to improve model generalization capability. However, challenges remain regarding computational complexity, detail preservation, and model performance under extreme low-light conditions. This study concludes that future LLIE research will focus on developing models that are more adaptive, efficient, lightweight, and robust for various computer vision applications.
Co-Authors Ade Bastian Ani Sanirah