基于二阶广义全变差的多帧图像超分辨率重建

doi:10.3969/j.issn.0372-2112.2015.07.004

电子学报 ›› 2015, Vol. 43 ›› Issue (7): 1275-1280.DOI: 10.3969/j.issn.0372-2112.2015.07.004

基于二阶广义全变差的多帧图像超分辨率重建

任福全¹, 邱天爽¹, 韩军², 金声³

1. 大连理工大学电子信息与电气工程学部, 辽宁大连 116024;
2. 大连现代高技术集团有限公司, 辽宁大连 116025;
3. 澳大利亚纽卡索大学信息技术学院, 纽卡索, 2300

收稿日期:2013-11-04 修回日期:2014-12-17 出版日期:2015-07-25
- 通讯作者:
- 邱天爽
- 作者简介:
- 任福全 男,1984年生于辽宁营口.现为大连理工大学博士研究生.研究方向为图像与视频处理,压缩感知. E-mail:renfu_quan@hotmail.com
- 基金资助:
- 国家自然科学基金 (No.81241059,No.61172108); 国际合作专项资助 (No.2012DFA10700)

Multiframe Image Super Resolution Based on Second Order Total Generalized Variation

REN Fu-quan¹, QIU Tian-shuang¹, HAN Jun², JIN Sheng³

1. Faculty of Electrical and Electronic Engine, Dalian University of Technology, Dalian, Liaoning 116024, China;
2. Dalian Modern High Techology Group Ltd, Dalian, Liaoning 116025, China;
3. School of Information Techrology, University of Newcastle, Newcastle 2300, Australia

Received:2013-11-04 Revised:2014-12-17 Online:2015-07-25 Published:2015-07-25
- Supported by:
- National Natural Science Foundation of China (No.81241059, No.61172108); International Cooperation Project (No.2012DFA10700)

摘要/Abstract

摘要：

图像超分辨率重建是图像处理领域的重要问题.本文将二阶广义全变差用于基于正则化的多帧图像超分辨率重建问题,构建了基于二阶广义全变差正则项的图像超分辨率模型.为了更好地保持重建图像的边缘和细节,采用图像空域自适应正则化参数,并针对该重建模型的非光滑性,给出了基于半二次正则化和交替方向法的求解算法.实验结果表明该模型和数值算法能够较好地提高图像的分辨率,同时可以较好地保持图像的细节信息.

关键词: 超分辨率重建, 二阶广义全变差, 自适应正则化参数, 半二次正则化, 交替方向法

Abstract:

Super resolution is a very important issue in the field of image processing.In this paper,Second order total general variation (TGV) is used to solve a reconstruction based multi-frame images super resolution problem.A second order total general variation based super resolution model is built in the paper.In order to preserve the edges and detail information,a spatial adaptive regularization parameter of the image is applied.For solving the non-smooth problem,a half quadric and alternating direction based numerical algorithm is proposed.Experiments verified that the proposed model and numerical algorithm can effectively increase the resolution of an image,and they can preserve the texture and detail information.

Key words: super resolution, second order total generalized variation, adaptive regularization parameter, half-quadratic regularization, alternating direction method

中图分类号:

TN 911.7

任福全, 邱天爽, 韩军, 等. 基于二阶广义全变差的多帧图像超分辨率重建[J]. 电子学报, 2015, 43(7): 1275-1280.

REN Fu-quan, QIU Tian-shuang, HAN Jun, et al. Multiframe Image Super Resolution Based on Second Order Total Generalized Variation[J]. Acta Electronica Sinica, 2015, 43(7): 1275-1280.

参考文献

[1] W T Freeman,et al.Example-based super-resolution[J].IEEE Computer Graphics and Applications,2002 22(2):56-65.
[2] Weisheng Dong,Lei Zhang,et al.Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J].IEEE Trans on Image Processing,2011,20(7):1838-1857.
[3] J Yang,J Wright,et al.Image super-resolution as sparse representation of raw image patches[A].IEEE CVPR[C]. Anchorage,AK,2008.1-8.
[4] 练秋生,张伟.基于图像块分类稀疏表示的超分辨率重构算法[J].电子学报,2012,40(5):920-925. Lian Qiu-sheng,Zhang Wei.Image super-resolution algorithms based on sparse represen-tation of classified image patches[J].Acta Electronica Sinica,2012,40(5):920-925.(in Chinese)
[5] 韩玉兵,束锋,孙锦涛,等.基于MG-CG算法的图像超分辨率重建[J].电子学报,2007,35(7):1394-1397. Han Yu-bing,Shu Feng,Sun Jin-tao,et al.Image super-resolution reconstruction based on MG-CG algorithm[J].Acta Electronica Sinica,2007,35(7):1394-1397.(in Chinese)
[6] 童基均,刘进,蔡强.基于全变差的加权最小二乘法PET图像重建[J].电子学报,2013,41(4):787-790. Tong Ji-jun,Liu Jin,Cai Qiang.The PET image reconstruction based on weighted least-squares and TV penalty[J].Acta Electronica Sinica,2013,41(4):787-790.(in Chinese)
[7] Kristian Bredies,Karl Kunisch,Thomas Pock.Total generalized variation[J].SIAM J Imaging Sci,2010,3(3):492-526.
[8] Florian Knoll,Kristian Bredies,Thomas Pock,et al.Second order total generalized variation (TGV) for MRI[J].Magnetic Resonance in Medicine,2011,65(2):480-491.
[9] Tuomo Valkonen,Kristian Bredies,Florian Knoll.Total generalized variation in diffusion tensor imaging[J].SIAM J Imaging Sci,2013,6(1):487-525.
[10] Ran He,Wei-shi Zheng,et al.Half-quadratic minimization for robust sparse representation[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2013,36(2):261-275.
[11] Bo Zhao,Justin P Haldar,et al.Image reconstruction from highly underdamped (k,t)-space data with joint partial separability and sparsity constraints[J].IEEE Trans on Med Imaging,2012,31(9):1809-1820.
[12] A Guitton,W W Symes.Robust and stable velocity analysis using the Huber function[J].Seg Technical Program Expanded Abstracts,1999,18(1):1166-1169.
[13] A T Puig,A Wiesel,A O Hero.Multidimen-signal shrinkage thresholding operator and group LASSO penalties [J].IEEE Signal Processing Letters,2011,18(6):363-366.
[14] A Marquina,S J Osher.Image super-resolution by TV-regularization and Bregman iteration[J].J Sci Comput,2008,37(3):367-382.
[15] Kristian Bredies,Yiqiu Dong,Michael Hintermüller.Spatially dependent regularization parameter selection in total generalized variation models for image restoration[J].International Journal of Computer Mathematics,2013,90(1):109-123.
[16] Atsunori Kanemura,Shin-ichi Maeda,Shin Ishii.Edge-preserving Bayesian image super resolution based on compound Markov random fields[J].Lecture Notes in Computer Science,2007,4669:611-620.
[17] Yiqiu Dong,Michael Hintermüller,et al.Automated regularization parameter selection in multi-scale total variation models for image restoration[J].International Journal of Computer Mathematics,2011,40(1):82-104.
[18] Z Wang,A C Bovik,H R Sheikh,E P Simoncelli.Image quality assessment:from error visibility to structural similarity[J].IEEE Trans on Image Processing,2004,13(4):600-612.
[19] J R Bergen,P Anandan,K J Hanna,et al.Hierachical model-based motion estimation[A].Proc Eur Conf Computer Vision[C].Springer-Verlag,1992.237-252.

基于二阶广义全变差的多帧图像超分辨率重建

Multiframe Image Super Resolution Based on Second Order Total Generalized Variation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

[1]	吴靖, 叶晓晶, 黄峰, 陈丽琼, 王志锋, 刘文犀. 基于深度学习的单帧图像超分辨率重建综述[J]. 电子学报, 2022, 50(9): 2265-2294.
[2]	王相海, 赵晓阳, 王鑫莹, 赵克云, 宋传鸣. 非抽取小波边缘学习深度残差网络的单幅图像超分辨率重建[J]. 电子学报, 2022, 50(7): 1753-1765.
[3]	伍政华, 孙明健, 顾宗山, 范明意. 基于二阶广义方向性全变分的图像超分辨率重建方法[J]. 电子学报, 2017, 45(11): 2625-2632.
[4]	杜奕, 张挺. 基于多点信息统计法的土地覆盖图像超分辨率重建方法[J]. 电子学报, 2016, 44(11): 2576-2582.
[5]	吴秀秀, 肖珊, 张煜. 基于配准的肺4D-CT图像超分辨率重建[J]. 电子学报, 2015, 43(2): 383-386.
[6]	韩玉兵, 束锋, 孙锦涛, 吴乐南. 基于MG-CG算法的图像超分辨率重建[J]. 电子学报, 2007, 35(7): 1394-1397.
[7]	张岩, 吕善伟. ADI-FDTD+GRT在波导电路分析中的应用[J]. 电子学报, 2005, 33(9): 1688-1690.
[8]	韩玉兵;陈小蔷;吴乐南. 一种视频序列的超分辨率重建算法[J]. 电子学报, 2005, 33(1): 126-130.
[9]	韩玉兵;陈小蔷;吴乐南. 一种视频序列的超分辨率重建算法[J]. 电子学报, 2004, 32(12): 2059-2062.