Real-time Visualization of High-Dynamic-Range Infrared Images based on Human Perception Characteristics - Noise Removal, Image Detail Enhancement and Time Consistency

Frederic Garcia, Cedric Schockaert, Bruno Mirbach

2015

Abstract

This paper presents an image detail enhancement and noise removal method that accounts for the limitations on human’s perception to effectively visualize high-dynamic-range (HDR) infrared (IR) images. In order to represent real world scenes, IR images use to be represented by a HDR that generally exceeds the working range of common display devices (8 bits). Therefore, an effective HDR compression without loosing the perceptibility of details is needed. We herein propose a practical approach to effectively map raw IR images to 8 bit data representation. To do so, we propose an image processing pipeline based on two main steps. First, the raw IR image is split into base and detail image components using the guided filter (GF). The base image corresponds to the resulting edge-preserving smoothed image. The detail image results from the difference between the raw and base images, which is further masked using the linear coefficients of the GF, an indicator of the spatial detail. Then, we filter the working range of the HDR along time to avoid global brightness fluctuations in the final 8 bit data representation, which results from combining both detail and base image components using a local adaptive gamma correction (LAGC). The last has been designed according to the human vision characteristics. The experimental evaluation shows that the proposed approach significantly enhances image details in addition to improving the contrast of the entire image. Finally, the high performance of the proposed approach makes it suitable for real word applications.

References

  1. Agaian, S., Panetta, K., and Grigoryan, A. (2001). Transform based image enhancement with performance measure. In IEEE Transactions on Image Processing, pages 367-381.
  2. Bradski, G. and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, 1st edition.
  3. Branchitta, F., Diani, M., Corsini, G., and Porta, A. (2008). Dynamic-range compression and contrast enhancement in infrared imaging systems. Optical Engineering, 47(7):076401:1-14.
  4. Branchitta, F., Diani, M., Corsini, G., and Romagnoli, M. (2009). New technique for the visualization of high dynamic range infrared images. Optical Engineering, 48(9):096401:1-9.
  5. Durand, F. and Dorsey, J. (2002). Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph., 21(3):257-266.
  6. Glushko, S. W. and Salvaggio, C. (2007). Quantitative analysis of infrared contrast enhancement algorithms. In Infrared Imaging Systems: Design, Analysis, Modeling, and Testing, pages 65430S:1-12.
  7. He, K., Sun, J., and Tang, X. (2013). Guided Image Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6):1397-1409.
  8. Karali, A. O., Okman, O. E., and Aytac, T. (2010). Adaptive enhancement of infrared images containing sea surface targets. In IEEE Signal Processing and Communications Applications Conference (SIU), pages 605- 608.
  9. Kim, J. Y., Kim, L. S., and Hwang, S. H. (2001). An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Systems for Video Technology, 11(4):475-484.
  10. Liang, K., Ma, Y., Xie, Y., Zhou, B., and Wang, R. (2012). A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Physics and Technology, 55(4):309-315.
  11. Liu, B., Wang, X., Jin, S., Chen, Y., Liu, C., and Liu, X. (2012). Infrared image detail enhancement based on local adaptive gamma correction. Chinese Optics Letters, 10(2):021002:1-5.
  12. Liu, N. and Zhao, D. (2014). Detail enhancement for highdynamic-range infrared images based on guided image filter. Infrared Physics and Technology, 67:138- 147.
  13. Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B. T. H., and Zimmerman, J. B. (1987). Adaptive Histogram Equalization and its Variations. Comput. Vision Graph. Image Process., 39(3):355-368.
  14. Silverman, J. (1993). Signal-processing algorithms for display and enhancement of ir images. In Infrared Technology, pages 440-450.
  15. Vickers, V. E. (1996). Plateau equalization algorithm for realtime display of highquality infrared imagery. Optical Engineering, 35(7):1921-1926.
  16. Zuiderveld, K. (1994). Graphics gems iv. In Heckbert, P. S., editor, Image Processing, chapter Contrast Limited Adaptive Histogram Equalization, pages 474- 485. Academic Press Professional, Inc.
  17. Zuo, C., Chen, Q., Liu, N., Ren, J., and Sui, X. (2011). Display and detail enhancement for highdynamic-range infrared images. Optical Engineering, 50(12):127401:1-9.
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Paper Citation


in Harvard Style

Garcia F., Schockaert C. and Mirbach B. (2015). Real-time Visualization of High-Dynamic-Range Infrared Images based on Human Perception Characteristics - Noise Removal, Image Detail Enhancement and Time Consistency . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 144-152. DOI: 10.5220/0005309501440152


in Bibtex Style

@conference{visapp15,
author={Frederic Garcia and Cedric Schockaert and Bruno Mirbach},
title={Real-time Visualization of High-Dynamic-Range Infrared Images based on Human Perception Characteristics - Noise Removal, Image Detail Enhancement and Time Consistency},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={144-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005309501440152},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Real-time Visualization of High-Dynamic-Range Infrared Images based on Human Perception Characteristics - Noise Removal, Image Detail Enhancement and Time Consistency
SN - 978-989-758-089-5
AU - Garcia F.
AU - Schockaert C.
AU - Mirbach B.
PY - 2015
SP - 144
EP - 152
DO - 10.5220/0005309501440152