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Aiming at the problems of low-resolution and poor visual quality of infrared images, a locality-constrained group sparsity based infrared image super-resolution algorithm is proposed. Firstly with considering the texture self-similarity of infrared images and group structural sparsity of atom coefficients, a locality-constrained group sparse (LCGS) model is proposed. Secondly, under LCGS and K-singular value decomposition, a pair of group structural dictionaries is learned. The dictionary pair can well capture and preserve the intrinsic geometrical manifold of low and high resolution data. Finally, the high-resolution infrared images are recovered by the high-resolution dictionary and the corresponding low-resolution group sparse coefficients. Experimental results show that the proposed method obtains excellent performance in objective evaluation and subjective visual effect.
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Keywords:
- infrared image /
- super-resolution /
- group sparse /
- dictionary learning
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[18] Wang J J, Yang J C, Yu K, L F J, Huang T, Gong Y H 2010 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition San Francisco, USA, June 13-18, 2010 p3360
[19] Majumda A, Ward R K 2009 Can. J. Elect. Comput. E 34 136
[20] Yang J, Zhang Y 2011 SIAM J. Sci. Comput. 33 250
[21] Xu Y, Zhong A N, Yang J, Zhang D 2011 Opt. Eng. 50 037202
[22] Brunet D, Vrscay E R, Wang Z 2012 IEEE Trans. Image Process. 21 1488
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[1] Kang M, Chaudhuri S 2003 IEEE Signal Proc. Mag. 20 1920
[2] Farsiu S, Robinson D, Elad M, Milanfar P 2004 Int. J. Imag. Syst. Tech. 14 47
[3] Elad M, Datsenko D 2007 Comput. J. 50 1
[4] Elad M, Figueiredo M A T, Ma Y 2010 P. IEEE 98 972
[5] Zhao J J, Tang Z Y, Yang J, Liu E Q, Zhou Y 2011 J. Infrared Millim. Waves 30 156 (in Chinese) [赵佳佳, 唐峥远, 杨杰, 刘尔琦, 周越 2011 红外与毫米波学报 30 156]
[6] Song C X, Ma K, Qin C, Xiao P 2013 Acta Phys. Sin. 62 040702 (in Chinese) [宋长新, 马克, 秦川, 肖鹏 2013 物理学报 62 040702]
[7] Ning F L, He B J, Wei J 2013 Acta Phys. Sin. 62 174212 (in Chinese) [宁方立, 何碧静, 韦娟 2013 物理学报 62 174212]
[8] Yang J, Wright J, Huang T 2010 IEEE Trans. Image Process. 19 2861
[9] Sun Y B, Wei Z H, Xiao L, Zhang Z R 2010 Acta Electron. Sin. 38 2898 (in Chinese) [孙玉宝, 韦志辉, 肖亮, 张峥嵘 2010 电子学报 38 2898]
[10] Lian Q S, Zhang W 2012 Acta Electron. Sin. 40 920 (in Chinese) [练秋生, 张伟 2012 电子学报 40 920]
[11] Yang S Y, Wang M, Chen Y G, et al 2012 IEEE Trans. Image Process. 21 4016
[12] Tang Y, Yuan Y, Yan P K, Li X L 2013 J. Vis. Commun. Image R 24 148
[13] Huang J Z, Zhang T, Metaxas D 2011 J. Mach. Learn. Res. 12 3371
[14] Sun H, Zhang Z L, Yu L 2012 Signal Process. 28 759 (in Chinese) [孙洪, 张智林, 余磊 2012 信号处理 28 759]
[15] Huang J, Zhang T 2010 Ann. Stat. 38 1978
[16] Liu H C, Li S T, Yin H T 2013 Opt. Commun. 289 45
[17] Chang H, Yeung D, Xiong Y 2004 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Washington DC, USA, June 27-July 2, 2004 p275
[18] Wang J J, Yang J C, Yu K, L F J, Huang T, Gong Y H 2010 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition San Francisco, USA, June 13-18, 2010 p3360
[19] Majumda A, Ward R K 2009 Can. J. Elect. Comput. E 34 136
[20] Yang J, Zhang Y 2011 SIAM J. Sci. Comput. 33 250
[21] Xu Y, Zhong A N, Yang J, Zhang D 2011 Opt. Eng. 50 037202
[22] Brunet D, Vrscay E R, Wang Z 2012 IEEE Trans. Image Process. 21 1488
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