Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2003 Papers
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Split Aperture Imaging for High Dynamic Range
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Approximation Techniques in Signal and Image Processing (SPIE Press Monograph Vol. PM157)
Local Approximation Techniques in Signal and Image Processing (SPIE Press Monograph Vol. PM157)
Optical Splitting Trees for High-Precision Monocular Imaging
IEEE Computer Graphics and Applications
Noise reduction in high dynamic range imaging
Journal of Visual Communication and Image Representation
High Dynamic Range Video
Clipped noisy images: Heteroskedastic modeling and practical denoising
Signal Processing
Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum
IEEE Transactions on Image Processing
A versatile HDR video production system
ACM SIGGRAPH 2011 papers
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images
IEEE Transactions on Image Processing
Joint demosaicing and denoising
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter
IEEE Transactions on Image Processing
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
IEEE Transactions on Image Processing
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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 (alignment) 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.