  One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, denoising, resampling (align- ment) and exposure fusion as separate
problems. In contrast, in this paper we present a unifying approach, performing
HDR assembly directly from raw sensor data. Our framework includes a camera
noise model adapted to HDR video and an algorithm for spatially adaptive HDR
reconstruction based on fitting of local polynomial approximations to observed
sensor data. The method is easy to implement and 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
existing methods, both in terms of flexibility and reconstruction quality.
