The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Resolusi super imej berasaskan rangkaian neural konvolusi (CNN) digunakan secara meluas sebagai teknik peningkatan imej berkualiti tinggi. Walau bagaimanapun, secara amnya, ia menunjukkan sedikit atau tiada isotropi pencahayaan. Oleh itu, kami mencadangkan dua kaedah, "Latihan Penyongsangan Penyongsangan (LIT)" dan "Purata Penyongsangan Penyongsangan (LIA)," untuk meningkatkan isotropi kecerahan resolusi super imej berasaskan CNN. Keputusan eksperimen pembesaran imej 2× menunjukkan purata nisbah isyarat kepada hingar puncak (PSNR) menggunakan Purata Penyongsangan Luminance adalah kira-kira 0.15-0.20dB lebih tinggi daripada resolusi super konvensional.
Kazuya URAZOE
Kobe University
Nobutaka KUROKI
Kobe University
Yu KATO
Kobe University
Shinya OHTANI
Kobe University
Tetsuya HIROSE
Kobe University
Masahiro NUMA
Kobe University
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Salinan
Kazuya URAZOE, Nobutaka KUROKI, Yu KATO, Shinya OHTANI, Tetsuya HIROSE, Masahiro NUMA, "Improvement of Luminance Isotropy for Convolutional Neural Networks-Based Image Super-Resolution" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 7, pp. 955-958, July 2020, doi: 10.1587/transfun.2019EAL2168.
Abstract: Convolutional neural network (CNN)-based image super-resolutions are widely used as a high-quality image-enhancement technique. However, in general, they show little to no luminance isotropy. Thus, we propose two methods, “Luminance Inversion Training (LIT)” and “Luminance Inversion Averaging (LIA),” to improve the luminance isotropy of CNN-based image super-resolutions. Experimental results of 2× image magnification show that the average peak signal-to-noise ratio (PSNR) using Luminance Inversion Averaging is about 0.15-0.20dB higher than that for the conventional super-resolution.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAL2168/_p
Salinan
@ARTICLE{e103-a_7_955,
author={Kazuya URAZOE, Nobutaka KUROKI, Yu KATO, Shinya OHTANI, Tetsuya HIROSE, Masahiro NUMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improvement of Luminance Isotropy for Convolutional Neural Networks-Based Image Super-Resolution},
year={2020},
volume={E103-A},
number={7},
pages={955-958},
abstract={Convolutional neural network (CNN)-based image super-resolutions are widely used as a high-quality image-enhancement technique. However, in general, they show little to no luminance isotropy. Thus, we propose two methods, “Luminance Inversion Training (LIT)” and “Luminance Inversion Averaging (LIA),” to improve the luminance isotropy of CNN-based image super-resolutions. Experimental results of 2× image magnification show that the average peak signal-to-noise ratio (PSNR) using Luminance Inversion Averaging is about 0.15-0.20dB higher than that for the conventional super-resolution.},
keywords={},
doi={10.1587/transfun.2019EAL2168},
ISSN={1745-1337},
month={July},}
Salinan
TY - JOUR
TI - Improvement of Luminance Isotropy for Convolutional Neural Networks-Based Image Super-Resolution
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 955
EP - 958
AU - Kazuya URAZOE
AU - Nobutaka KUROKI
AU - Yu KATO
AU - Shinya OHTANI
AU - Tetsuya HIROSE
AU - Masahiro NUMA
PY - 2020
DO - 10.1587/transfun.2019EAL2168
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2020
AB - Convolutional neural network (CNN)-based image super-resolutions are widely used as a high-quality image-enhancement technique. However, in general, they show little to no luminance isotropy. Thus, we propose two methods, “Luminance Inversion Training (LIT)” and “Luminance Inversion Averaging (LIA),” to improve the luminance isotropy of CNN-based image super-resolutions. Experimental results of 2× image magnification show that the average peak signal-to-noise ratio (PSNR) using Luminance Inversion Averaging is about 0.15-0.20dB higher than that for the conventional super-resolution.
ER -