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

PDF(2076 KB)

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

学术论文

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

作者信息 +

Multiframe Image Super Resolution Based on Second Order Total Generalized Variation

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

Author information +

文章历史 +

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.

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.

导出引用

任福全, 邱天爽, 韩军, 金声. 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. https://doi.org/10.3969/j.issn.0372-2112.2015.07.004

REN Fu-quan, QIU Tian-shuang, HAN Jun, JIN Sheng. Multiframe Image Super Resolution Based on Second Order Total Generalized Variation[J]. Acta Electronica Sinica, 2015, 43(7): 1275-1280. https://doi.org/10.3969/j.issn.0372-2112.2015.07.004

中图分类号： TN 911.7

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.