In image super-resolution (SR), one tries
to obtain a high resolution image from several low-resolution (LR)
images with overlapping fields of view. SR algorithms first
estimate the relative disparity among the different LR images to
achieve a precise registration and then try to obtain a high-resolution
image by properly combining the registered images. In this project we
are investigating the use of new sampling schemes to achieve a very
accurate registration of very low-resolution multi-view images and then
we achieve super-resolution by exploiting some a-priori knowledge of
the properties of the original image (See the figures below for an
To probe further,
check-out our video showing the performance of our
super-resolution algorithm in Loic Baboulaz homepage.
|(b) ROI of (a)
|(c) Super-res. image (1024x1024)
- L. Baboulaz and P.L. Dragotti, Exact Feature Extraction using Finite Rate of Innovation Principles with an Application to Image Super-resolution, IEEE Transactions on Image Processing, to appear.
- L.Baboulaz and P.L. Dragotti, Local Feature Extraction for Image Super-Resolution, in Proc. of IEEE International Conference on Image Processing (ICIP), San Antonio,
Texas, September 2007.
- L. Baboulaz and P.L. Dragotti, Distributed
Acquisition and Image Super-Resolution Based on Continuous
Moments from Samples, in Proc. of IEEE International Conference on
Image Processing (ICIP), Atlanta, USA, October 2006 (to appear).
Student: Loic Baboulaz.