A Unified Framework for Multi-Sensor HDR Video Reconstruction
This work addresses the problem of high-quality HDR video capture for camera systems with multiple sensors, offering an incremental improvement by unifying previously separate processing steps.
The paper tackles the challenge of HDR video reconstruction from multi-sensor setups by proposing a unified framework that performs HDR assembly directly from raw sensor data, achieving real-time performance for a 4 Mpixel system and showing advantages in flexibility and quality over existing methods.
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.