Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

Abstract

Aiming at solving the problem of low resolution and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism (HVPM) was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets (POCS) reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super-resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approximately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.

Keywords

human vision processing mechanism (HVPM) / projection onto convex sets (POCS) / super-resolution / infrared image / reconstruction algorithm

References

[1] Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview. Signal Processing Magazine, IEEE, 2003, 20(3): 21–36

[2] Tang L. Research on multi-frame image super-resolution reconstruction. Dissertation for the Doctoral Degree. Zhejiang: Zhejiang University, 2011

[3] Pei Z J. The creation of retinal information processing model. Dissertation for the Doctoral Degree. Tianjin: Tianjin Medical University, 2011

[4] Ratliff F, Hartline H K. The responses of Limulus optic nerve fibers to patterns of illumination on the receptor mosaic. The Journal of General Physiology, 1959, 42(6): 1241–1255

[5] Hartline H K. The receptive fields of optic nerve fibers. American Journal of Physiology–Legacy Content, 1940, 130(4): 690–699

[6] Hui Chen, Kai Ouyang. Simulation Comparison of Lateral Inhibition Models in Image Enhancement. Journal of System Simulation, 2003, 15(1): 100–103

[7] Wang Q, He Y Q, Zhou Y C. The infrared image enhancement based on wavelet transformation and lateral inhibition network. Infrared Technology, 2011, 33(9): 541–544 (in Chinese)

[8] Deng G, Cahill L W. An adaptive Gaussian filter for noise reduction and edge detection. In: Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference. 1993, 1615–1619

[9] Xiao J X. Super-resolution image reconstruction based on POCS algorithm. Dissertation for the Doctoral Degree. Shanghai: Shanghai Jiao Tong University, 2009

[10] Li X L, Tao D C. A multi-frame image super-resolution method. Signal Processing, 2010, 90(2): 405–414 (in Chinese)

[11] Sutour C, Aujol J F, Deledalle C A. Adaptive regularization of the NL-means: Application to image and video denoising. Hal-00854830, 2013, 2(10): 1–15

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61275099 and 61102131), the Project of Key Laboratory of Signal and Information Processing of Chongqing (No. CSTC2009CA2003), the Natural Science Foundation of Chongqing Science and Technology Commission (Nos. CSTC2010BB2411, CSTC2010BB2398 and CSTC2006BB2373), the Natural Science Foundation of Chongqing Municipal Education Commission (Nos. KJ060509 and KJ080517), the Science and Technique Foundation of Chongqing (CSTC) (No. 2011AB2008), and the Natural Science Foundation of Chongqing University of Posts and Telecommunications (CQUPT) (Nos. A2006-04 and A2006-86).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg