Author
Özkanoglu, M. A., Özer, Sedat
Publication Date
2022-03
Publication Place
-
Elsevier
Subject
Domain transfer, GANs, Infrared image generation
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0167-8655
Record ID
199c2bb5-6bd3-42cd-b316-6117cfbdcf56
Library Location
Computer Science
Date
2022-03
Notes
TÜBİTAK
Sample Text
Utilizing both visible and infrared (IR) images in various deep learning based computer vision tasks has been a recent trend. Consequently, datasets having both visible and IR image pairs are desired in many applications. However, while large image datasets taken at the visible spectrum can be found in many domains, large IR-based datasets are not easily available in many domains. The lack of IR counterparts of the available visible image datasets limits existing deep algorithms to perform on IR images effectively. In this paper, to overcome with that challenge, we introduce a generative adversarial network (GAN) based solution and generate the IR equivalent of a given visible image by training our deep network to learn the relation between visible and IR modalities. In our proposed GAN architecture (InfraGAN), we introduce using structural similarity as an additional loss function. Furthermore, in our discriminator, we do not only consider the entire image being fake or real but also each pixel being fake or real. We evaluate our comparative results on three different datasets and report the state of the art results over five metrics when compared to Pix2Pix and ThermalGAN architectures from the literature. We report up to +16% better performance in Structural Similarity Index Measure (SSIM) over Pix2Pix and +8% better performance over ThermalGAN for VEDAI dataset. Further gains on different metrics and on different datasets are also reported in our experiments section.
DOI
10.1016/j.patrec.2022.01.026
Cilt
155
