SRNSSI: A Deep Light-Weight Network for Single Image Super Resolution Using Spatial and Spectral Information

被引：17
作者：
Esmaeilzehi, Alireza [1 ]
Ahmad, M. Omair [1 ]
Swamy, M. N. S. [1 ]
机构：
[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada
来源：
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING | 2021年 / 7卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
Image Super Resolution; Convolutional Neural Networks; Residual Learning; Deep Learning; SUPERRESOLUTION;
D O I：
10.1109/TCI.2021.3070522
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Design of a residual block that provides a rich set of features while requiring only small numbers of parameters and operations is crucial for the task of single image super resolution. This is especially important in applications with limited power and storage capacity. In this paper, a new multi-domain residual block is proposed in order to generate richer set of features for the task of image super resolution. The proposed residual block consists of two feature generation modules. The first one is a spatial information processing module and the second one is a spectral information processing module. The feature maps obtained by these two feature generation modules are concatenatively fused to obtain block's output. The new residual block is used to build light-weight super resolution networks. Extensive experiments are performed using several benchmark datasets in order to evaluate the performance of the networks using the new multi-domain residual block. It is shown that the use of both the spatial and spectral features enhances the performance of the light-weight super resolution networks.
引用
收藏
页码：409 / 421
页数：13
相关论文
共 50 条
[11]   Second-order Attention Network for Single Image Super-Resolution [J].
Dai, Tao ;
Cai, Jianrui ;
Zhang, Yongbing ;
Xia, Shu-Tao ;
Zhang, Lei .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :11057-11066
[12]   Accelerating the Super-Resolution Convolutional Neural Network [J].
Dong, Chao ;
Loy, Chen Change ;
Tang, Xiaoou .
COMPUTER VISION - ECCV 2016, PT II, 2016, 9906 :391-407
[13]   Image Super-Resolution Using Deep Convolutional Networks [J].
Dong, Chao ;
Loy, Chen Change ;
He, Kaiming ;
Tang, Xiaoou .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (02) :295-307
[14]   Deep Back-Projection Networks For Super-Resolution [J].
Haris, Muhammad ;
Shakhnarovich, Greg ;
Ukita, Norimichi .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :1664-1673
[15]  
He K, P IEEE C COMP VIS PA, P770, DOI [DOI 10.1109/CVPR.2016.90, 10.1109/CVPR.2016.90]
[16]   Deep Level Sets for Salient Object Detection [J].
Hu, Ping ;
Shuai, Bing ;
Liu, Jun ;
Wang, Gang .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :540-549
[17]   Fast and Accurate Single Image Super-Resolution via Information Distillation Network [J].
Hui, Zheng ;
Wang, Xiumei ;
Gao, Xinbo .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :723-731
[18]   Geometry-Aware Distillation for Indoor Semantic Segmentation [J].
Jiao, Jianbo ;
Wei, Yunchao ;
Jie, Zequn ;
Shi, Honghui ;
Lau, Rynson ;
Huang, Thomas S. .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :2864-2873
[19]  
Kim Jang- Hyun, 2018, ARXIV181208914
[20]  
Kim J, 2016, PROC CVPR IEEE, P1637, DOI [10.1109/CVPR.2016.182, 10.1109/CVPR.2016.181]
12345