14 4

A New Deep Learning Based Multi-Spectral Image Fusion Method

Title
A New Deep Learning Based Multi-Spectral Image Fusion Method
Author
신현철
Keywords
image fusion; visible; infrared; convolutional neural network; Siamese network
Issue Date
2019-06
Publisher
MDPI
Citation
ENTROPY, v. 21, No. 6, Article no. 570
Abstract
In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality.
URI
https://www.mdpi.com/1099-4300/21/6/570http://repository.hanyang.ac.kr/handle/20.500.11754/117013
ISSN
1099-4300
DOI
10.3390/e21060570
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
Files in This Item:
2019.06_신현철_A New Deep Learning Based Multi-Spectral Image Fusion Method.pdfDownload
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE