Low-Light Image Enhancement and Target Detection Based on Deep Learning

Author:

Yao Zhuo

Abstract

Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.

Publisher

International Information and Engineering Technology Association

Subject

Electrical and Electronic Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A novel low light object detection method based on the YOLOv5 fusion feature enhancement;Scientific Reports;2024-02-23

2. GUI Interface Design for Defect Recognition and Rating of X-ray Weld Images based on Image Preprocessing;2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI);2023-10-28

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