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
최근에는 단일 영상의 초해상도를 구현하는 최첨단 방법으로 SRCNN(Super-Resolution Convolutional Neural Network)이 널리 알려져 있다. 그러나 SRCNN에는 재기(jaggy) 및 링잉(ringing) 아티팩트와 같은 성능 문제가 존재합니다. 또한, 4K/8K 60fps 등 고해상도 영상 스트림에 대한 실시간 업컨버팅 시스템을 구현하기 위해서는 처리 지연 및 구현 비용 등의 문제가 남아있다. 본 논문에서는 합성곱 신경망(CNN)이 아닌 패치 기반 심층신경망(SR-PDNN)을 통한 고성능 초해상도를 제안한다. SR-PDNN은 매우 간단한 엔드투엔드 학습 시스템에도 불구하고 기존 CNN 기반 접근 방식보다 더 높은 성능을 달성합니다. 또한 이 시스템은 ASIC(주문형 집적회로) 또는 FPGA(필드 프로그래밍 가능 게이트 어레이)를 사용한 하드웨어 구현으로 초저지연 비디오 처리에 적합합니다.
Reo AOKI
Kanazawa University,Visual Technologies (ASIC)
Kousuke IMAMURA
Kanazawa University
Akihiro HIRANO
Kanazawa University
Yoshio MATSUDA
Kanazawa University
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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},}
부
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 -