Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
ACM SIGGRAPH 2007 papers
Demosaicing algorithms for area- and line-scan cameras in print inspection
Journal of Visual Communication and Image Representation
Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum
IEEE Transactions on Image Processing
Flexible voxels for motion-aware videography
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
On Plenoptic Multiplexing and Reconstruction
International Journal of Computer Vision
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
A unified framework for multi-sensor HDR video reconstruction
Image Communication
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Multisampled 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 multisampled imaging. We briefly describe how multisampling 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, multisampled images can be better interpolated using local structural models that are learned offline 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 1) traditional color imaging with a mosaic of color filters, 2) high dynamic range monochrome imaging using a mosaic of exposure filters, and 3) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.