[1]张圣祥,郑力新,朱建清,等.采用深度学习的快速超分辨率图像重建方法[J].华侨大学学报(自然科学版),2019,40(2):245-250.[doi:10.11830/ISSN.1000-5013.201804064]
　ZHANG Shengxiang,ZHENG Lixin,ZHU Jianqing,et al.Fast Super-Resolution Image Reconstruction Method Using Deep Learning[J].Journal of Huaqiao University(Natural Science),2019,40(2):245-250.[doi:10.11830/ISSN.1000-5013.201804064]
点击复制

采用深度学习的快速超分辨率图像重建方法()
分享到：

《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第40卷
期数:
2019年第2期
页码:
245-250
栏目:
出版日期:
2019-03-20

文章信息/Info

Title:
Fast Super-Resolution Image Reconstruction Method Using Deep Learning
文章编号:
1000-5013(2019)02-0245-06
作者:
张圣祥1; 2;  郑力新1; 2;  朱建清1; 2;  潘书万1; 2
1. 华侨大学 工学院, 福建 泉州 362021; 2. 华侨大学 工业智能化与系统福建省高校工程研究中心, 福建 泉州 362021
Author(s):
ZHANG Shengxiang1; 2;  ZHENG Lixin1; 2; ZHU Jianqing1; 2;  PAN Shuwan1; 2
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. Industrial Intelligence and System Fujian University Engineering Research Center, Huaqiao University, Quanzhou 362021, China
关键词:
超分辨率图像重建;  深度学习;  卷积神经网络;  级联
Keywords:
super-resolution image reconstruction;  deep learning;  convolutional neural network;  cascade
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201804064
文献标志码:
A
摘要:
为满足实际工业生产需要,提出一种基于深度学习的快速超分辨率图像重建方法.采用一种快速的卷积神经网络结构,使用级联的小卷积核以取得重建速度上的提升,加深卷积网络以取得重建质量上的提升.实验结果表明:在标准的公共数据集上,该算法重建的高分辨率图像在主观视觉感受和客观的图像质量评价(峰值信噪比)上取得较好的效果,且重建时间大大缩短;将算法应用在实际的项目中,能达到阈值分割后准确检测物体的标准,减少企业对高额工业相机的经济开支.
Abstract:
In order to meet the needs of actual industrial production, a fast super-resolution image reconstruction method based on deep learning is proposed. We proposed our own convolutional neural network structure, using cascaded small convolution kernels to achieve a higher reconstruction speed, and deepening the convolution network to achieve an improvement in reconstruction quality. The experimental results show that on the standard public dataset, the high-resolution image reconstructed by the our algorithm achieves better results in subjective visual perception and objective image quality evaluation(peak signal-to-noise ratio), at the mean time, the reconstruction time is greatly shortened. The algorithm is applied in projects to solve the problem for accurately detecting objects after threshold segmentation. In this way, it also reduces the high expenses of enterprises for purchasing industrial cameras.

参考文献/References:

[1] 曾凯,丁世飞.图像超分辨率重建的研究进展[J].计算机工程与应用,2017,53(16):29-35.DOI:10.3778/j.issn.1002-8331.1705-0097.
[2] YANG C Y,MA Chao,YANG M H.Single-image super-resolution: A benchmark[C]//European Conference on Computer Vision.Switzerland:Springer,2014:372-386.DOI:10.1007/978-3-319-10593-2_25.
[3] YANG Jianchao,WRIGHT J,HUANG T,et al.Image super-resolution as sparse representation of raw image patches[C]//IEEE Conference on Computer Vision and Pattern Recognition.Anchorage:IEEE Press,2008:1-8.DOI:10.1109/CVPR.2008.4587647.
[4] HE Hu,KONDI L P.A regularization framework for joint blur estimation and super-resolution of video sequences[C]//Proceedings of the 2005 IEEE International Conference on Image Processing.Genova:IEEE Press,2005:329-332.DOI:10.1109/ICIP.2005.1530395.
[5] LI Xiaoguang,LAM K M,QIU G P,et al.Examplebased image super-resolution with class-speciflc predictors[J].Journal of Visual Communication and Image Representation,2009,20(5):312-322.DOI:10.1016/j.jvcir.2009.03.008.
[6] SU Congyong,ZHUANG Yueting,LI Huang,et al.Steerable pyramid-based face hallucination[J].Pattern Recognition,2005,38(6):813-824.DOI:10.1016/j.patcog.2004.11.007.
[7] JIJI C V,CHAUDHURI S.Single-frame image super-resolution through contourlet learning[C]//EURASIP Journal on Advances in Signal Processing.New York:Springer,2006:235.DOI:10.1155/ASP/2006/73767.
[8] 张晓燕,秦龙龙,钱渊,等.一种改进的稀疏表示超分辨率重建算法[J].重庆邮电大学学报(自然科学版),2016,28(3):400-405.DOI:10.3979/j.issn.1673-825X.2016.03.020.
[9] FREEMAN W T,PASZTOR E C,CARMICHAEL O T.Learning low-level vision[J].International Journal of Computer Vision,2000,40(1):25-47.DOI:10.1109/ICCV.1999.790414.
[10] FREEMAN W T,JONE T R,PASZTOR E C.Example-based super-resolution[J].IEEE Computer Graphics and Applications,2002,22(2):56-65.DOI:10.1109/38.988747.
[11] DONG Chao,CHEN C L,HE Kaiming,et al.Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):295-307.DOI:10.1109/TPAMI.2015.2439281.
[12] 刘晨羽,蒋云飞,李学明.基于卷积神经网的单幅图像超分辨率重建算法[J].计算机辅助设计与图形学学报,2017,29(9):1643-1649.DOI:10.3969/j.issn.1003-9775.2017.09.007.
[13] KIM J,LEE J K,LEE K M.Accurate image super-resolution using very deep convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:1646-1654.DOI:10.1109/CVPR.2016.182.
[14] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J/OL].Computer Science,2014.[2017-06-10] .http://arxiv.org/pdf/1409.1556.
[15] SHI Wenzhe,CABALLERO J,HUSZAR F,et al.Real-time single image and video super-resolution using an efficient subpixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognitionhttp.Las Vegas:IEEE Press,2016:1874-1883.DOI:10.1109/CVPR.2016.207.
[16] OTSU N.A threshold selection method from gray-level histograms[J].Automatica,1979,9(1):62-66.DOI:10.1109/TSMC.1979.4310076.
[17] XU Xiaohong,WU Zhihui,CHEN Yu,et al.Plant root spatial distribution measurements based on the hough transformation[J].Neurocomputing,2014,145:209-220.DOI:10.1016/j.neucom.2014.05.041.

相似文献/References:

[1]钟必能,潘胜男.选择性搜索和多深度学习模型融合的目标跟踪[J].华侨大学学报(自然科学版),2016,37(2):207.[doi:10.11830/ISSN.1000-5013.2016.02.0207]
　ZHONG Bineng,PAN Shengnan.Multi-Clue Fusion Target Tracking Algorithm Based on Selective Search and Deep Learning[J].Journal of Huaqiao University(Natural Science),2016,37(2):207.[doi:10.11830/ISSN.1000-5013.2016.02.0207]
[2]邹辉,杜吉祥,翟传敏,等.深度学习与一致性表示空间学习的跨媒体检索[J].华侨大学学报(自然科学版),2018,39(1):127.[doi:10.11830/ISSN.1000-5013.201508047]
　ZOU Hui,DU Jixiang,ZHAI Chuanmin,et al.Cross-Modal Multimedia Retrieval Based Deep Learning and Shared Representation Space Learning[J].Journal of Huaqiao University(Natural Science),2018,39(2):127.[doi:10.11830/ISSN.1000-5013.201508047]
[3]胡珉,周显威,高新闻.公路隧道视频预处理和病害识别算法[J].华侨大学学报(自然科学版),2020,41(5):595.[doi:10.11830/ISSN.1000-5013.202002024]
　HU Min,ZHOU Xianwei,GAO Xinwen.Video Preprocess and Defect Recognition Algorithm for Road Tunnel[J].Journal of Huaqiao University(Natural Science),2020,41(2):595.[doi:10.11830/ISSN.1000-5013.202002024]
[4]马迎杰,王佳斌,郑力新,等.深度可分离卷积网络的驾驶状态识别算法[J].华侨大学学报(自然科学版),2021,42(2):259.[doi:10.11830/ISSN.1000-5013.202001010]
　MA Yingjie,WANG Jiabin,ZHENG Lixin,et al.Driving State Recognition Algorithm Based on Deep Separable Convolutional Network[J].Journal of Huaqiao University(Natural Science),2021,42(2):259.[doi:10.11830/ISSN.1000-5013.202001010]
[5]叶靓玲,李伟达,郑力新,等.结合目标检测与特征匹配的多目标跟踪算法[J].华侨大学学报(自然科学版),2021,42(5):661.[doi:10.11830/ISSN.1000-5013.202105018]
　YE Liangling,LI Weida,ZHENG Lixin,et al.Multiple Object Tracking Algorithm Based on Detection and Feature Matching[J].Journal of Huaqiao University(Natural Science),2021,42(2):661.[doi:10.11830/ISSN.1000-5013.202105018]
[6]周密,张维纬,陶英杰,等.采用可替代滤波器的卷积神经网络模型剪枝方法[J].华侨大学学报(自然科学版),2022,43(2):245.[doi:10.11830/ISSN.1000-5013.202011013]
　ZHOU Mi,ZHANG Weiwei,TAO Yingjie,et al.Pruning Method of Convolutional Neural Network Using Replaceable Filter[J].Journal of Huaqiao University(Natural Science),2022,43(2):245.[doi:10.11830/ISSN.1000-5013.202011013]

备注/Memo

备注/Memo:
收稿日期: 2018-04-19
通信作者: 郑力新(1967-),男,教授,博士,主要从事光电检测与智能计算的研究.E-mail:zlxgxy@hqu.edu.cn.
基金项目: 国家自然科学基金青年科学基金资助项目(61602191); 福建省厦门市科技计划项目(3502Z20173045); 福建省泉州市高层次人才创新创业项目(2017G036)
更新日期/Last Update: 2019-03-20