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Oh TH, Lee JY, Tai YW, et al. Robust High Dynamic Range Imaging by Rank Minimization. IEEE Trans Pattern Anal Mach Intell. 2015;37(6):1219-32doi: 10.1109/TPAMI.2014.2361338.
Oh, T. H., Lee, J. Y., Tai, Y. W., & Kweon, I. S. (2015). Robust High Dynamic Range Imaging by Rank Minimization. IEEE transactions on pattern analysis and machine intelligence, 37(6), 1219-32. https://doi.org/10.1109/TPAMI.2014.2361338
Oh, Tae-Hyun, et al. "Robust High Dynamic Range Imaging by Rank Minimization." IEEE transactions on pattern analysis and machine intelligence vol. 37,6 (2015): 1219-32. doi: https://doi.org/10.1109/TPAMI.2014.2361338
Oh TH, Lee JY, Tai YW, Kweon IS. Robust High Dynamic Range Imaging by Rank Minimization. IEEE Trans Pattern Anal Mach Intell. 2015 Jun;37(6):1219-32. doi: 10.1109/TPAMI.2014.2361338. PMID: 26357344.
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IEEE Trans Pattern Anal Mach Intell. 2015 Jun;37(6):1219-32. doi: 10.1109/TPAMI.2014.2361338.

Robust High Dynamic Range Imaging by Rank Minimization.

IEEE transactions on pattern analysis and machine intelligence

Tae-Hyun Oh, Joon-Young Lee, Yu-Wing Tai, In So Kweon

PMID: 26357344 DOI: 10.1109/TPAMI.2014.2361338

Abstract

This paper introduces a new high dynamic range (HDR) imaging algorithm which utilizes rank minimization. Assuming a camera responses linearly to scene radiance, the input low dynamic range (LDR) images captured with different exposure time exhibit a linear dependency and form a rank-1 matrix when stacking intensity of each corresponding pixel together. In practice, misalignments caused by camera motion, presences of moving objects, saturations and image noise break the rank-1 structure of the LDR images. To address these problems, we present a rank minimization algorithm which simultaneously aligns LDR images and detects outliers for robust HDR generation. We evaluate the performances of our algorithm systematically using synthetic examples and qualitatively compare our results with results from the state-of-the-art HDR algorithms using challenging real world examples.

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