Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Image enhancement,Low-light conditions,Deep learning models,Object detection and classification,Empirical comparison
论文评审过程：Received 31 October 2021, Revised 30 May 2022, Accepted 15 August 2022, Available online 28 August 2022, Version of Record 13 September 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116848
