[1]黄炜钦,黄德天,顾培婷,等.非局部相似和双边滤波的图像超分重建[J].华侨大学学报(自然科学版),2018,39(6):926-931.[doi:10.11830/ISSN.1000-5013.201702043]
　HUANG Weiqin,HUANG Detian,GU Peiting,et al.Image Super-Resolution Reconstruction Based on Non-Local Similarity and Bilateral Filter[J].Journal of Huaqiao University(Natural Science),2018,39(6):926-931.[doi:10.11830/ISSN.1000-5013.201702043]
点击复制

非局部相似和双边滤波的图像超分重建()
分享到：

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

卷:
第39卷
期数:
2018年第6期
页码:
926-931
栏目:
出版日期:
2018-11-20

文章信息/Info

Title:
Image Super-Resolution Reconstruction Based on Non-Local Similarity and Bilateral Filter
文章编号:
1000-5013(2018)06-0926-06
作者:
黄炜钦1;  黄德天1; 2;  顾培婷1;  柳培忠1;  骆炎民2
1. 华侨大学 工学院, 福建 泉州 362021;2. 华侨大学 机电及自动化学院, 福建 厦门 361021
Author(s):
HUANG Weiqin1;  HUANG Detian1; 2;  GU Peiting1;  LIU Peizhong1;  LUO Yanmin2
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
关键词:
图像处理;  超分辨率;  稀疏表示;  非局部相似性;  双边滤波
Keywords:
image processing;  super-resolution;  sparse representation;  non-local similarity;  bilateral filter
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201702043
文献标志码:
A
摘要:
为了提高重建图像的分辨率,提出一种改进的稀疏表示超分重建算法.在稀疏编码阶段,引入非局部相似正则化以改进稀疏编码目标函数,并通过非局部相似正则化获得图像非局部冗余,以保持图像边缘信息.为了进一步恢复图像的边缘细节信息,提出一种基于改进双边滤波的全局误差补偿模型,以实现重建图像的误差补偿.实验结果表明:与Bicubic,L1SR,SISR,ANR,NE+LS,NE+NNLS,NE+LLE和A+(16 atoms)等算法相比,无论在主观视觉效果,还是在峰值信噪比和结构相似性指标上,所提算法都有显著的提高.
Abstract:
In order to enhance the resolution of reconstructed images, an improved image super-resolution reconstruction algorithm based on sparse representation is proposed. In the sparse coding stage, the non-local(NL)similarity regularization is introduced to improve the sparse coding objective function. The NL redundancy is obtained by the NL similarity regularization to preserve the edge information of the image. To restore edge details further, a global error compensation model based on improved bilateral filter is proposed to realize the error compensation of the reconstructed images. Experimental results validate that compared with Bicubic, L1SR, SISR, ANR, NE+LS, NE+NNLS, NE+LLE and A+(16 atoms)algorithms, the proposed approach has a remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual effects.

参考文献/References:

[1] YANG J,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.Alaska:IEEE Press,2008:1-8.
[2] YANG J,WRIGHT J,HUANG T S,et al.Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[3] YANG S,WANG M,CHEN Y,et al.Single-Image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding[J].IEEE Transactions on Image Processing,2012,21(9):4016-4028.
[4] 丁静,王培康.M-估计耦合双边滤波的正则化超分辨率重建[J].计算机应用,2010,30(11):3005-3007.
[5] DONOHO D L.For most large underdetermined systems of equations, the minimal 1-norm near-solution approximates the sparsest near-solution[J].Communications on Pure and Applied Mathematics,2006,59(7):907-934.
[6] 沈丰,赵宇明.基于实时Retinex与双边滤波的图像增强算法[J].计算机应用与软件,2009,26(11):234-238.
[7] MAIRAL J,BACH F,PONCE J,et al.Non-local sparse models for image restoration[J].IEEE International Conference on Computer Vision,2009,30(2):2272-2279.
[8] CHA Y,KIM S.The error-amended sharp edge(EASE)scheme for image zooming[J].IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society,2007,16(6):1496-1505.
[9] 余博,郭雷,钱晓亮,等.一种新的自适应双边滤波算法[J].应用科学学报,2012,30(5):517-523.
[10] CHEN K.Adaptive smoothing via contextual and local discontinuities[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1552-1567.
[11] QIAN Xiaoliang,GUO Lei,YU Bo.Scale-based adaptive smoothing[C]//International Conference on Future Computer and Communication.Wuhan:IEEE Press,2010:V2(40-44).DOI:10.1109/ICFCC.2010.5497294.
[12] TIMOFTE R,SMET V D,GOOL L V.A+: Adjusted anchored neighborhood regression for fast super-resolution[C]//Asian Conference on Computer Vision.[S.l.]:Springer,2014:111-126.DOI:10.1007/978-3-319-16817-3_8.
[13] ZEYDE R,ELAD M,PROTTER M.On single image scale-up using sparse-representations[C]//International Conference on Curves and Surfaces.Berlin:Springer,2010:711-730.DOI:10.1007/978-3-642-27413-8_47.
[14] TIMOFTE R,DE V,GOOL L V.Anchored neighborhood regression for fast example-based super-resolution[C]//IEEE International Conference on Computer Vision.Sydney:IEEE Press,2013:1920-1927.DOI:10.1109/ICCV.2013.241.

相似文献/References:

[1]郑玉燕,何建农.软形态学的多结构加权均值形态滤波算法[J].华侨大学学报(自然科学版),2008,29(4):535.[doi:10.11830/ISSN.1000-5013.2008.04.0535]
　ZHENG Yu-yan,HE Jian-nong.Omnidirectional Weighted Combination Morphological Filter Based on Soft Morphology[J].Journal of Huaqiao University(Natural Science),2008,29(6):535.[doi:10.11830/ISSN.1000-5013.2008.04.0535]
[2]张健,黄德天,林炎明.采用稀疏表示和小波变换的超分辨率重建算法[J].华侨大学学报(自然科学版),2020,41(2):250.[doi:10.11830/ISSN.1000-5013.201908005]
　ZHANG Jian,HUANG Detian,LIN Yanming.Super-Resolution Reconstruction Algorithm Using Sparse Representation and Wavelet Transform[J].Journal of Huaqiao University(Natural Science),2020,41(6):250.[doi:10.11830/ISSN.1000-5013.201908005]

备注/Memo

备注/Memo:
收稿日期: 2017-02-22
通信作者: 黄德天(1985-),男,讲师,博士,主要从事计算机视觉、机器学习和嵌入式系统的研究.E-mail:huangdetian@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61203242); 福建省中青年教师教育科研基金资助项目(JAT170053); 华侨大学研究生科研创新能力培育计划资助项目(1511422002)
更新日期/Last Update: 2018-11-20