Unified HDR reconstruction from raw CFA data

IEEE International Conference on Computational Photography (ICCP) - 2013
Download the publication : iccppaper_for_review.pdf [15.8Mo]  
HDR reconstruction from multiple exposures poses several challenges. Previous HDR reconstruction techniques have considered debayering, denoising, resampling (alignment) and exposure fusion in several steps. We instead present a unifying approach, performing HDR assembly directly from raw sensor data in a single processing operation. Our algorithm includes a spatially adaptive HDR reconstruction based on fitting local polynomial approximations to observed sensor data, using a localized likelihood approach incorporating spatially varying sensor noise. We also present a realistic camera noise model adapted to HDR video. The method allows reconstruction to an arbitrary resolution and output mapping. We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system. We further show that our algorithm has clear advantages over state-of-the-art methods, both in terms of flexibility and reconstruction quality.

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Presentation at ICCP 2013 :

BibTex references

@Article\{KGBU13,
  author       = "Kronander, Joel and Gustavson, Stefan and Bonnet, Gerhard and Unger, Jonas",
  title        = "Unified HDR reconstruction from raw CFA data",
  journal      = "IEEE International Conference on Computational Photography (ICCP)",
  year         = "2013"
}

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