InfraGAN: A GAN architecture to transfer visible images to infrared domain

Name: InfraGAN: A GAN architecture to transfer visible images to infrared domain
Author: Özkanoglu, M. A., Özer, Sedat

İsim	InfraGAN: A GAN architecture to transfer visible images to infrared domain
Yazar	Özkanoglu, M. A., Özer, Sedat
Basım Tarihi:	2022-03
Basım Yeri	- Elsevier
Konu	Domain transfer, GANs, Infrared image generation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0167-8655
Kayıt Numarası	199c2bb5-6bd3-42cd-b316-6117cfbdcf56
Lokasyon	Computer Science
Tarih	2022-03
Notlar	TÜBİTAK
Örnek Metin	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

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InfraGAN: A GAN architecture to transfer visible images to infrared domain

Yazar Özkanoglu, M. A., Özer, Sedat

Basım Tarihi 2022-03

Basım Yeri - Elsevier

Konu Domain transfer, GANs, Infrared image generation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0167-8655

Kayıt Numarası 199c2bb5-6bd3-42cd-b316-6117cfbdcf56

Lokasyon Computer Science

Tarih 2022-03

Notlar TÜBİTAK

Örnek Metin 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