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Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels
April 2005 (vol. 27 no. 4)
pp. 518-530
ASCII Text
x 
 
Srinivasa G. Narasimhan, Shree K. Nayar, "Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 518-530, April, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.76,
author = {Srinivasa G. Narasimhan and Shree K. Nayar},
title = {Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {4},
issn = {0162-8828},
year = {2005},
pages = {518-530},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.76},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels
IS - 4
SN - 0162-8828
SP518
EP530
EPD - 518-530
A1 - Srinivasa G. Narasimhan,
A1 - Shree K. Nayar,
PY - 2005
KW - Image formation
KW - multisampling
KW - dynamic range
KW - color
KW - resolution
KW - interpolation
KW - structural models
KW - learning
KW - Bayer pattern.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
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.

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Index Terms:
Image formation, multisampling, dynamic range, color, resolution, interpolation, structural models, learning, Bayer pattern.
Citation:
Srinivasa G. Narasimhan, Shree K. Nayar, "Enhancing Resolution Along Multiple Imaging Dimensions Using Assorted Pixels," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 518-530, April 2005, doi:10.1109/TPAMI.2005.76
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