Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Programmable Imaging: Towards a Flexible Camera
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
Optical Splitting Trees for High-Precision Monocular Imaging
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2007 courses
Adaptive dynamic range camera with reflective liquid crystal
Journal of Visual Communication and Image Representation
Computational Photography: Epsilon to Coded Photography
Emerging Trends in Visual Computing
Technical Section: Hyper-Resolution: Image detail reconstruction through parametric edges
Computers and Graphics
Less is more: coded computational photography
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Efficient graphical models for processing images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Video and image bayesian demosaicing with a two color image prior
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
High-quality image deblurring with panchromatic pixels
ACM Transactions on Graphics (TOG)
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Multi-sampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green and blue spectral filters found in most solid-state color cameras is one example of multi-sampled imaging. We briefly describe how multi-sampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and hence image quality). One can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. Hence, multi-sampled images can be better interpolated using local structural models that are learned off-line from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image intensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are (a) traditional color imaging with a mosaic of color filters, (b) high dynamic range monochrome imaging using a mosaic of exposure filters, and (c) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.