Earticle

초록

영어

In this paper, a deep learning based fusion technique is presented for the visible and infrared image fusion. In general, the image fusion process is composed of three stages: feature extraction by an encoder, feature fusion, and the reconstruction of the fused image by a decoder. We propose a feature fusion scheme that gives spatially adaptive weights to each infrared and visible pair in the fusion process. Features of the infrared image are used to determine the weights based on the observation that only the high activation region in IR contains the salient information. We conduct both quantitative and qualitative analysis on two datasets. Experimental results show that our fusion method achieves better performance than the previous method.

목차

Abstract
1. Introduction
2. Proposed Fusion Method
2.1. Training
2.2. Fusion Layer
3. Experiments
3.1. Experimental setup
3.2. Experimental result
4. Conclusions
Acknowledgement
References

키워드

저자

• Minahil Syeda Zille [ Dept. of Intelligent Systems and Robotics Chungbuk National University Cheongju, South Korea. ]
• Jun-Hyung Kim [ Dept. of Intelligent Systems and Robotics Chungbuk National University Cheongju, South Korea. ]
• Youngbae Hwang [ Dept. of Intelligent Systems and Robotics Chungbuk National University Cheongju, South Korea. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004