Citation: Rasheed, M.T.; Guo, G.; Shi, D.; Khan, H.; Cheng, X. An Empirical Study on Retinex Methods for Low-Light Image Enhancement. Remote Sens. 2022, 14, 4608. https://doi.org/10.3390/rs14184608 Academic Editor: Gwanggil Jeon Received: 7 August 2022 Accepted: 11 September 2022 Published: 15 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). remote sensing Article An Empirical Study on Retinex Methods for Low-Light Image Enhancement Muhammad Tahir Rasheed 1,† , Guiyu Guo 1,† , Daming Shi 1, *, Hufsa Khan 1 and Xiaochun Cheng 2 1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China 2 Computer Science Department, Middlesex University, Hendon, London NW4 4BT, UK * Correspondence: dshi@szu.edu.cn † These authors contributed equally to this work. Abstract: A key part of interpreting, visualizing, and monitoring the surface conditions of remote- sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory- based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods. Keywords: low-light image enhancement; retinex theory; deep learning; remote-sensing 1. Introduction Low-light enhancement methodologies try to recover buried details, remove the noise, restore the color details, and increase the dynamic range and contrast of the low- light images. Low light has inescapable effects on remote monitoring equipment and computer vision tasks. Low signal-to-noise ratio (SNR) causes severe noise in low-light imaging and makes it difficult to extract features for interpreting remote-sensing via computer vision tasks, whereas the performance of computer vision tasks entirely depends on accurate feature extraction [1]. Remote-sensing image enhancement has a wide range of applications in object detection [2,3], object tracking [4–7], video surveillance [8,9], military applications, daily life [10–14], atmospheric sciences [15], driver assistance systems [16], and agriculture. Earth is continuously being monitored by analyzing the images taken by satellites. Analyzing remotely taken images to help in fire detection, flood prediction, and understanding other environmental issues. Low-light enhancement of these images is playing a vital role in understanding these images in a better way. Even the accuracy of other remote sensing algorithms, such as classification and object detection, depends entirely on the image’s quality. In the literature, different methodologies exist for enhancing such degraded low-light images. Retinex theory-based enhancement methods are widely accepted among these enhancement methodologies due to their robustness. The main purpose of this study is to compare the Retinex-based methods with other non-Retinex- based enhancement methods experimentally. For comparison, we have categorized all the enhancement methods into two major groups (i.e., Retinex-based and non-Retinex- based methods). The Retinex group includes classical and deep learning-based Retinex enhancement methods. Meanwhile, the non Retinex group includes histogram equalization, gamma correction, fusion, and deep learning-based enhancement methods. According to Retinex theory [17], an image can be decomposed into reflectance and illumination component. The reflectance component is considered an intrinsic component Remote Sens. 2022, 14, 4608. https://doi.org/10.3390/rs14184608 https://www.mdpi.com/journal/remotesensing