Paper Info
Title
Penrose Pixels Super-Resolution in the Detector Layout Domain
Abstract
We present a novel approach to reconstruction based super- resolution that explicitly models the detector's pixel layout. Pixels in our model can vary in shape and size, and there may be gaps between adjacent pixels. Furthermore, their layout can be periodic as well as aperiodic, such as penrose tiling or a biological retina. We also present a new variant of the well known error back-projection super-resolution algorithm that makes use of the exact detector model in its back projection operator for better accuracy. Our method can be applied equally well to either periodic or aperiodic pixel tiling. Through analysis and extensive testing using synthetic and real images, we show that our approach outperforms existing reconstruction based algorithms for regular pixel arrays. We obtain significantly better results using aperiodic pixel layouts. As an interesting example, we apply our method to a retina-like pixel structure modeled by a centroidal Voronoi tessellation. We demonstrate that, in principle, this structure is better for super-resolution than the regular pixel array used in today's sensors.
Year
DOI
Venue
2007
10.1109/ICCV.2007.4408888
ICCV
Keywords
Field
DocType
computational geometry,eye,image reconstruction,image resolution,aperiodic pixel tiling,biological retina,centroidal Voronoi tessellation,detector layout domain,error backprojection super-resolution algorithm,image reconstruction,penrose pixels,penrose tiling,pixel arrays,pixel layout,retina-like pixel structure
Iterative reconstruction,Computer vision,Centroidal Voronoi tessellation,Computer science,Penrose tiling,Artificial intelligence,Pixel,Real image,Aperiodic graph,Detector,Image resolution
Conference
Volume
Issue
ISSN
2007
1
1550-5499
Citations 
PageRank 
References 
26
1.49
10
Authors
3
Name
Order
Citations
PageRank
Moshe Ben-ezra163146.56
Zhouchen Lin24805203.69
Bennett Wilburn384363.95