Edge preserving super-resolution infrared image reconstruction based on L1- and L2-norms

Abstract

Super-resolution (SR) is a widely used technology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared (IR) SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the quantitative measures, but they mostly fail to preserve edge and visual quality. This paper proposes an algorithm based on high frequency layer features. This algorithm focuses on the IR image edge texture in the reconstruction process. Experimental results show that the mean gradient of the IR image reconstructed by the proposed algorithm increased by 1.5, 1.4, and 1.2 times than that of the traditional algorithm based on L1-norm, L2-norm, and traditional mixed norm, respectively. The peak signal-to-noise ratio, structural similarity index, and visual effect of the reconstructed image also improved.

Keywords

infrared (IR) super-resolution (SR) image / reconstruction / high frequency layer / edge texture

References

[1] Lei Y G, Wang R R, Chen M H. Development of foreign uncooled IRFPA retectors. Laser & Infrared, 2007, 37(9): 801–805

[2] Suo J L, Ji X Y, Dai Q H. An overview of computational photography. Science China, Information Sciences, 2012, 55(6): 1229–1248

[3] Sui X, Gao H, Sun Y, Chen Q, Gu G. Infrared super-resolution imaging method based on retina micro-motion. Infrared Physics & Technology, 2013, 60(5): 340–345

[4] Sui X, Chen Q, Gu G, Shen X. Infrared super-resolution imaging based on compressed sensing. Infrared Physics & Technology, 2014, 63(11): 119–124

[5] Tsai R Y, Huang T S. Multiframe image restoration andregistration. In: Proceedings of Advances in Computer Vision and Image Processing, 1984

[6] Zhan M Q, Deng Z L. L1 norm of total variation regularization based superresolution reconstruction for images. Science Technology and Engineering, 2010, 28(10): 6903–6906

[7] Yi H, Chen D, Li W, Zhu S, Wang X, Liang J, Tian J. Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study. Journal of Biomedical Optics, 2013, 18(5): 056013

[8] Jiang M X, Wang H Y, Wang J, Wang B. Visual object tracking algorithm based on ML and L2-norm. Acta Electronica Sinica, 2013, 41(11): 2307–2313

[9] Lu Q, Hu F. Improved image super-resolution reconstruction algorithm based on L1-norm. Radio Engineering of China, 2009, 39(9): 13–15

[10] Li Y H,  Lyu X Q , YuH F .Sequence image super-resolution reconstruction base on L1 and L2 mixed norm. Journal of Computer Applications, 2015, 35(3): 840–843

[11] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine, 2003, 20(4): 21–36

[12] Li T, Papamichalis P E. Image super-resolution reconstruction based on adaptive gradient field sharpening. In: Proceedings of 18th IEEE International Conference on Digital Signal Processing (DSP), 2013, 1–6

[13] Xu J, Chang Z, Fan J, Zhao X, Wu X, Wang Y. Image superresolution by midfrequency sparse representation and total variation regularization. Journal of Electronic Imaging, 2015, 24(1): 013039

[14] Chen W C, Chen H H. Image super-resolution reconstruction based on high-and mid-frequency components.  Journal of Hangzhou Dianzi University (Social Sciences), 2015(1): 49–52

[15] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600–612

[16] Shen Q, Zhao X. An adaptive super-resolution reconstruction of image local texture feature. Electro-Optic Technology Application, 2015, 30(4): 24–26,50

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61275099 and 61671094) and the Natural Science foundation of Chongqing Science and Technology Commission (No. CSTC2015JCYJA40032).

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg