IEICE TRANSACTIONS on Information
High-Performance Super-Resolution via Patch-Based Deep Neural Network for Real-Time Implementation
Summary :
Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.11 pp.2808-2817
- Publication Date
- 2018/11/01
- Publicized
- 2018/08/20
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7081
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Reo AOKI
Kanazawa University,Visual Technologies (ASIC)
Kousuke IMAMURA
Kanazawa University
Akihiro HIRANO
Kanazawa University
Yoshio MATSUDA
Kanazawa University
Keyword
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Reo AOKI, Kousuke IMAMURA, Akihiro HIRANO, Yoshio MATSUDA, "High-Performance Super-Resolution via Patch-Based Deep Neural Network for Real-Time Implementation" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 11, pp. 2808-2817, November 2018, doi: 10.1587/transinf.2018EDP7081.
Abstract: Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7081/_p
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@ARTICLE{e101-d_11_2808,
author={Reo AOKI, Kousuke IMAMURA, Akihiro HIRANO, Yoshio MATSUDA, },
journal={IEICE TRANSACTIONS on Information},
title={High-Performance Super-Resolution via Patch-Based Deep Neural Network for Real-Time Implementation},
year={2018},
volume={E101-D},
number={11},
pages={2808-2817},
abstract={Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).},
keywords={},
doi={10.1587/transinf.2018EDP7081},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - High-Performance Super-Resolution via Patch-Based Deep Neural Network for Real-Time Implementation
T2 - IEICE TRANSACTIONS on Information
SP - 2808
EP - 2817
AU - Reo AOKI
AU - Kousuke IMAMURA
AU - Akihiro HIRANO
AU - Yoshio MATSUDA
PY - 2018
DO - 10.1587/transinf.2018EDP7081
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2018
AB - Recently, Super-resolution convolutional neural network (SRCNN) is widely known as a state of the art method for achieving single-image super resolution. However, performance problems such as jaggy and ringing artifacts exist in SRCNN. Moreover, in order to realize a real-time upconverting system for high-resolution video streams such as 4K/8K 60 fps, problems such as processing delay and implementation cost remain. In the present paper, we propose high-performance super-resolution via patch-based deep neural network (SR-PDNN) rather than a convolutional neural network (CNN). Despite the very simple end-to-end learning system, the SR-PDNN achieves higher performance than the conventional CNN-based approach. In addition, this system is suitable for ultra-low-delay video processing by hardware implementation using an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).
ER -
English
