Young Researcher Paper Award 2022
🥇Winners

Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 35, Number 6(3) (2023)
Copyright(C) MYU K.K.
pp. 2111-2128
S&M3310 Research Paper of Special Issue
https://doi.org/10.18494/SAM4296
Published: June 30, 2023

Applying Depthwise Separated Neural Network with Color Space Adjustment to Auto-colorization of Thermal Infrared Images [PDF]

Ming-Tsung Yeh, Wei-Yin Lo, Yi-Nung Chung, and Pei-Syuan Lu

(Received December 30, 2022; Accepted June 6, 2023)

Keywords: auto-colorization, color space adjustment, CAE, GAN, thermal infrared image

A general surveillance camera with a near-infrared illuminator provides a night vision function, but it is difficult to take a picture under foggy or smoky conditions, in a heavy rainfall environment, or under direct exposure to the sun because of poor object temperature reflection. A thermal infrared (TIR) camera can have better imaging to reflect objects in bad environments, and they have many applications in safe driving and military and scientific fields for all-weather surveillance. However, TIR images are mainly presented in grayscale, which causes the applications of TIR images to be limited and used only for rough object recognition. In previous studies, auto-colorization by predicting luminance and chrominance from grayscale images at the same time was typically performed, but the results were always blurry and abnormally colorized images. This study proposes the Depthwise Separated Colorization Generative Adversarial Network (DSCGAN) to colorize TIR images and overcome these drawbacks. Initially, the preprocessing light channel convolutional autoencoder (PLCAE) is proposed to generate the predicted L channel of the International Commission on Illumination LAB color space (CIELAB) that is used to restore some lost luminance information. Then, this predicted L channel is used as input to the proposed Colorization Generative Adversarial Network (CGAN) to create the AB channel. Finally, the data from L, A, and B channels are converted to the RGB visible light image. The experimental results indicate that our proposed PLCAE can efficiently enhance luminance details and achieve an accuracy rate of 0.9773. The proposed CGAN advances colorization accuracy and improves the peak signal-to-noise ratio (PSNR) to more than 26 dB. The colorized TIR images have almost the same color as the visible light images and clearly maintain object textures and details.

Corresponding author: Ming-Tsung Yeh


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Ming-Tsung Yeh, Wei-Yin Lo, Yi-Nung Chung, and Pei-Syuan Lu, Applying Depthwise Separated Neural Network with Color Space Adjustment to Auto-colorization of Thermal Infrared Images, Sens. Mater., Vol. 35, No. 6, 2023, p. 2111-2128.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Innovations of Sensor Applications and Related Technologies in IoT
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Special Issue on Advanced Data Sensing and Processing Technologies for Smart Community and Smart Life
Guest editor, Tatsuya Yamazaki (Niigata University)
Call for paper


Special Issue on Advanced Sensing Technologies and Their Applications in Human/Animal Activity Recognition and Behavior Understanding
Guest editor, Kaori Fujinami (Tokyo University of Agriculture and Technology)
Call for paper


Special Issue on Sensors and Artificial Intelligence for Smart Education Environments : Part 2
Guest editor, Chih Hsien Hsia (National Ilan University)
Call for paper


Special Issue on Advanced Micro/Nanomaterials for Various Sensor Applications (Selected Papers from ICASI 2022/2023)
Guest editor, Sheng-Joue Young (National United University)
Conference website
Call for paper


Special Issue on Advanced Micro/Nanomaterials for Various Sensor Applications (Selected Papers from ICASI 2024)
Guest editor, Sheng-Joue Young (National United University)
Conference website
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.