Convolutional neural network-based infrared image super resolution under low light environment

Tae Young Han; Yong Jun Kim; Byung Cheol Song

doi:10.23919/EUSIPCO.2017.8081318

Convolutional neural network-based infrared image super resolution under low light environment

Tae Young Han, Yong Jun Kim, Byung Cheol Song

Inha University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Convolutional neural networks (CNN) have been successfully applied to visible image super-resolution (SR) methods. In this paper, for up-scaling near-infrared (NIR) image under low light environment, we propose a CNN-based SR algorithm using corresponding visible image. Our algorithm firstly extracts high-frequency (HF) components from low-resolution (LR) NIR image and its corresponding high-resolution (HR) visible image, and then takes them as the multiple inputs of the CNN. Next, the CNN outputs HR HF component of the input NIR image. Finally, HR NIR image is synthesized by adding the HR HF component to the up-scaled LR NIR image. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods in terms of qualitative as well as quantitative metrics.

Original language	English
Title of host publication	25th European Signal Processing Conference, EUSIPCO 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	803-807
Number of pages	5
ISBN (Electronic)	9780992862671
DOIs	https://doi.org/10.23919/EUSIPCO.2017.8081318
State	Published - 23 Oct 2017
Event	25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: 28 Aug 2017 → 2 Sep 2017

Publication series

Name	25th European Signal Processing Conference, EUSIPCO 2017
Volume	2017-January

Conference

Conference	25th European Signal Processing Conference, EUSIPCO 2017
Country/Territory	Greece
City	Kos
Period	28/08/17 → 2/09/17

Bibliographical note

Publisher Copyright:
© EURASIP 2017.

Keywords

Convolutional neural networks
Low light images
Near-infrared and visible images
Super-resolution

Access to Document

10.23919/EUSIPCO.2017.8081318

Cite this

Han, T. Y., Kim, Y. J., & Song, B. C. (2017). Convolutional neural network-based infrared image super resolution under low light environment. In 25th European Signal Processing Conference, EUSIPCO 2017 (pp. 803-807). Article 8081318 (25th European Signal Processing Conference, EUSIPCO 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/EUSIPCO.2017.8081318

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title = "Convolutional neural network-based infrared image super resolution under low light environment",

abstract = "Convolutional neural networks (CNN) have been successfully applied to visible image super-resolution (SR) methods. In this paper, for up-scaling near-infrared (NIR) image under low light environment, we propose a CNN-based SR algorithm using corresponding visible image. Our algorithm firstly extracts high-frequency (HF) components from low-resolution (LR) NIR image and its corresponding high-resolution (HR) visible image, and then takes them as the multiple inputs of the CNN. Next, the CNN outputs HR HF component of the input NIR image. Finally, HR NIR image is synthesized by adding the HR HF component to the up-scaled LR NIR image. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods in terms of qualitative as well as quantitative metrics.",

keywords = "Convolutional neural networks, Low light images, Near-infrared and visible images, Super-resolution",

author = "Han, {Tae Young} and Kim, {Yong Jun} and Song, {Byung Cheol}",

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doi = "10.23919/EUSIPCO.2017.8081318",

language = "English",

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Han, TY, Kim, YJ & Song, BC 2017, Convolutional neural network-based infrared image super resolution under low light environment. in 25th European Signal Processing Conference, EUSIPCO 2017., 8081318, 25th European Signal Processing Conference, EUSIPCO 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 803-807, 25th European Signal Processing Conference, EUSIPCO 2017, Kos, Greece, 28/08/17. https://doi.org/10.23919/EUSIPCO.2017.8081318

Convolutional neural network-based infrared image super resolution under low light environment. / Han, Tae Young; Kim, Yong Jun; Song, Byung Cheol.
25th European Signal Processing Conference, EUSIPCO 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 803-807 8081318 (25th European Signal Processing Conference, EUSIPCO 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Han, Tae Young

AU - Kim, Yong Jun

AU - Song, Byung Cheol

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Y1 - 2017/10/23

N2 - Convolutional neural networks (CNN) have been successfully applied to visible image super-resolution (SR) methods. In this paper, for up-scaling near-infrared (NIR) image under low light environment, we propose a CNN-based SR algorithm using corresponding visible image. Our algorithm firstly extracts high-frequency (HF) components from low-resolution (LR) NIR image and its corresponding high-resolution (HR) visible image, and then takes them as the multiple inputs of the CNN. Next, the CNN outputs HR HF component of the input NIR image. Finally, HR NIR image is synthesized by adding the HR HF component to the up-scaled LR NIR image. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods in terms of qualitative as well as quantitative metrics.

AB - Convolutional neural networks (CNN) have been successfully applied to visible image super-resolution (SR) methods. In this paper, for up-scaling near-infrared (NIR) image under low light environment, we propose a CNN-based SR algorithm using corresponding visible image. Our algorithm firstly extracts high-frequency (HF) components from low-resolution (LR) NIR image and its corresponding high-resolution (HR) visible image, and then takes them as the multiple inputs of the CNN. Next, the CNN outputs HR HF component of the input NIR image. Finally, HR NIR image is synthesized by adding the HR HF component to the up-scaled LR NIR image. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods in terms of qualitative as well as quantitative metrics.

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Convolutional neural network-based infrared image super resolution under low light environment

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