TienDaiIFHDR

Project Title

Image Fusion for High Dynamic Range/ All-in-focus Applications

Introduction

Dynamic range of a scene is defined as the ratio of the highest to the lowest luminance. The real world scenes often have a very wide range of luminance, sometimes exceeding 10 orders of magnitude. Fig. 1 shows a high dynamic range ( HDR ) scene with a dynamic range of about 167, 470:1. To reproduce these scenes presents a challenge for conventional digital capture and display devices, which suffer a limited dynamic range of only 2 orders of magnitude.

The most common solution to address this problem is to take a sequence of low dynamic range ( LDR ) images of the same scene under different exposure intervals to capture all the radiance information and then render the captured stack to display. There are generally two pipelines. One way is to firstly estimate the camera response function from the image sequence to recover the true radiance of the original scene ( recorded as a 32 bit float radiance map ) [ 1, 2 ], and then tone map the created radiance map for display on LDR reproduction media ( usually 8 bit per channel ) [ 3, 4, 5 ]. Although this way gives very satisfying result, it's computationally expensive and time consuming. The other way is to fuse the captured images directly without the intermediate step of creating radiance map [ 6, 7 ], which is usually referred as "Exposure Fusion ( EF )" [7]. EF produces HDR-like images, which are comparable to those tone-mapped results, at a much lower computational cost. Due to its effectiveness and computational efficiency, EF is adopted by most of HDR applications on mobile platform, which has limited computational power [8].

Fig.1. Multi-exposed image stack of a high dynamic range scene

Methods

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Results

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Conclusions

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References

[1] P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs”, Proc. ACM SIGGRAPH’97, pp. 369 – 378, 1997. [2] T. Mitsunaga, S. K. Nayar, Radiometric self calibration,“Proceedings of the Computer Vision and Pattern Recognition, vol.1, 1999, pp.374–380. [3] G. Ward, A contrast-based scalefactor for luminance display, in: Graphics Gems IV, Academic Press, 1994, pp. 415–421. [4] F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images”, ACM Trans. Graph. (special issue SIGGRAPH 2002) 21, 3, 257-266, 2002. [5] Q. Tian, J. Duan, M. Chen and T. Peng, "Segmentation Based Tone-mapping for High Dynamic Range Images", Advances Concepts for Intelligent Vision Systems, pp.360-371, 2011. [6] A. Goshtasby. Fusion of multi-exposure images. Image and Vision Computing, 23:611–618, 2005. [7] Mertens, T. and Kautz, J. and Van Reeth, F. “Exposure fusion”, Computer Graphics and Applications, 2007. PG'07. 15th Pacific Conference on, 382--390, 2007. [8] Natasha Gelfand, Andrew Adams, Sung Hee Park, and Kari Pulli, “Multiexposure imaging on mobile devices,” in Proc. of the ACM Multimedia, 2010. [9] Vaquero, D. and Gelfand, N. and Tico, M. and Pulli, K. and Turk, M., “Generalized Autofocus”, Applications of Computer Vision (WACV), 2011 IEEE Workshop on, pp. 511--518, 2011. [10] G. Ward. Fast, robust image registration for compositing high dynamic range photographcs from hand-held exposures. Journal of Graphics Tools: JGT, 8(2):17–30, 2003. [11] J. M. Ogden, E. H. Adelson, J. R. Bergen, and P. J. Burt. Pyramid-based computer graphics. RCA Engineer, 30(5), 1985.

Appendix I - Code and Data

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Appendix II - Work Partition

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