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Low-light image (LLI) enhancement is an important image processing task that aims at improving the illumination of images taken under low-light conditions. Recently, a remarkable progress has been made in utilizing deep learning (DL) approaches for LLI enhancement. This paper provides a concise and comprehensive review and comparative study of the most recent DL models used for LLI enhancement. To our knowledge, this is the first comparative study dedicated to DL-based models for LLI enhancement. We address LLI enhancement in two ways: (i) standalone, as a separate task, and (ii) end-to-end, as a pre-processing stage embedded within another high-level computer vision task, namely object detection and classification. The paper consists of six logical parts. First, we provide an overview of the background and literature in LLI enhancement. Second, we describe the test data and experimental setup of the study. Third, we present a quantitative and qualitative comparison of the visual and perceptual quality achieved by 10 of the most recent DL-based LLI enhancement models. Fourth, we present a comparative analysis for object detection and classification performance achieved by 4 different object detection models applied on LLIs and their enhanced counterparts. Fifth, we perform a feature analysis of DL feature maps extracted from normal, low-light, and enhanced images, and perform the occlusion experiment to better understand the effect of LLI enhancement on the object detection and classification task. Finally, we provide our conclusions and highlight future steps and potential directions. • Evaluates Deep Learning (DL) models for Low-light Image (LLI) enhancement. • Compares 10 LLI enhancement models and 4 object detection and classification models. • Provides a quantitative and qualitative comparison of visual and perceptual quality. • Evaluates impact of LLI enhancement on object detecting and classification quality. • Performs occlusion experiment to study LLI enhancement's effect on object detection. [ABSTRACT FROM AUTHOR]

Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning approaches. However, most existing techniques are developed as standalone solutions and do not take into account the impact of LLI enhancement on high-level computer vision tasks like object classification. In this paper, we propose a new LLI enhancement model titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning and is designed for seamless integration into classification models. Through this integration, the classification model is embedded with an internal enhancement capability and is jointly trained to optimize both image enhancement and classification performance. We have conducted a large battery of experiments using SICE, Pascal VOC, and ExDark datasets, to quantitatively and qualitatively evaluate our approach's enhancement quality and classification performance. When evaluated as a standalone enhancement model, our solution consistently ranks among the best existing image enhancement techniques. When embedded with a classification model, our solution achieves an average 5.5% improvement in classification accuracy, compared with the traditional pipeline of separate enhancement followed by classification. Results produce robust classification quality on both LLIs and normal-light images (NLIs), and highlight a clear improvement to the literature. • Introduces Low-Light Image (LLI) Enhancement model for Object Classification task. • Introduces LLHFNet deep learning model for image-to-frequency filter learning. • Introduces LLI enhancer–classifier to integrate LLHFNet in object classification. • Performs joint training to optimize both enhancement and classification models. • Evaluates impact of LLI enhancement on object detecting and classification quality. • Produces 5.5% improvement in classification accuracy on Pascal VOC & ExDark LLIs. [ABSTRACT FROM AUTHOR]