@inproceedings{43ed1d1045f94d3e90be758fbde9108a,
title = "Deep Residual Neural Network Design for Super-Resolution Imaging",
abstract = "Convolution neural network recently confirmed the high-quality reconstruction for single-image super-resolution (SR). In this paper we present a Deep Level Residual Network (DLNR), a low-memory effective neural network to reconstruct super-resolution images. This neural network also has the following characteristics. (1) Ability to perform different convolution size operations on the image which can achieve more comprehensive feature extraction effects. (2) Using residual learning to expand the depth of the network and increase the capacity of learning. (3) Taking the skill of parameter sharing between the network module to reduce the number of parameters. After the experiment, we find that DLNR can achieve 37.78 in PSNR and 0.975 in SSIM when using Manga109 as testing set for 2× SR.",
author = "Chen, {Wei Ting} and Chen, {Pei Yin} and Lin, {Bo Chen}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 23rd International Computer Symposium, ICS 2018 ; Conference date: 20-12-2018 Through 22-12-2018",
year = "2019",
doi = "10.1007/978-981-13-9190-3_9",
language = "English",
isbn = "9789811391897",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "95--105",
editor = "Chuan-Yu Chang and Chien-Chou Lin and Horng-Horng Lin",
booktitle = "New Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers",
address = "Germany",
}