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Low-Resolution Infrared and High-Resolution Visible Image Fusion Based on U-NET

Lin, Hsuan
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1658755887078397

Abstract Details

2022, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
With current sensor technology, visible wavelength (VIS) images can be acquired at very high resolutions (HR) compared to the infrared (IR) images. Therefore, image fusion techniques aim to augment IR images with the superior spatial resolution of VIS images to overcome the resolution problems in IR imaging. This thesis introduces two ways to integrate IR and VIS images, IR image super-resolution and IR and VIS image fusion. The first application is super-resolution (SR) for IR images. We propose an IR image SR algorithm based on U-Net. By fusing the HR image features of the VIS images, the network can produce an IR SR image successfully and efficiently. Secondly, we also propose a novel framework for combining VIS and IR images, guided by feature extraction techniques such as VGG16. By designing the algorithm to preserve the meaningful VGG16 features from both IR and VIS images, the proposed method achieves excellent performance in the qualitative and quantitative aspects. In addition, we propose joint super-resolution and image fusion between IR and VIS images. Finally, we developed a new HR VIS and LR IR image pair dataset. Since this data collection closely resembles the real-world sensing scenarios, it is a valuable resource for continued exploration of this image processing field.
Keigo Hirakawa (Committee Chair)
Bradley Ratliff (Committee Member)
Vijayan Asari (Committee Member)
68 p.
Electrical Engineering
Image Fusion; Super-Resolution; Infrared Image; Visible Wavelength Image; Convolutional Neural Network

Recommended Citations

Citations

  • Lin, H. (2022). Low-Resolution Infrared and High-Resolution Visible Image Fusion Based on U-NET [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1658755887078397

    APA Style (7th edition)

  • Lin, Hsuan. Low-Resolution Infrared and High-Resolution Visible Image Fusion Based on U-NET. 2022. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1658755887078397.

    MLA Style (8th edition)

  • Lin, Hsuan. "Low-Resolution Infrared and High-Resolution Visible Image Fusion Based on U-NET." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1658755887078397

    Chicago Manual of Style (17th edition)

dayton1658755887078397
146
© 2022, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.