Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

作     者：Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin 

作者机构：National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany 

出 版 物：《IEEE Transactions on Circuits and Systems for Video Technology》 (IEEE Trans Circuits Syst Video Technol)

年 卷 期：2024年第34卷第2期

页      面：852-865页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 070207[理学-光学] 0808[工学-电气工程] 08[工学] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：This work was supported by the Alltek Marine Electronics Corporation (AMEC) through the Project "A Deep Learning-Based Ship Positioning and Tracking System" under Grant NTUST-AMEC-No. 10308 

主　　题：Neural networks 

摘      要：Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.