Unified HDR reconstruction from raw CFA data
2013 (English)In: Proceedings of IEEE International Conference on Computational Photography 2013 / [ed] David Boas, Paris Sylvain, Shmel Peleg, Todd Zickler, IEEE , 2013, 1-9 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2013. 1-9 p.
Engineering and Technology Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-90106DOI: 10.1109/ICCPhot.2013.6528315ISBN: 978-1-4673-6463-8OAI: oai:DiVA.org:liu-90106DiVA: diva2:612077
5th IEEE International Conference on Computational Photography, ICCP 2013; Cambridge, MA; United States