• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Multi-Frame Demosaicing and Super-Resolution from Under-Sampled Color Images (2004)

Cached

  • Download as a PDF

Download Links

  • [www.cs.technion.ac.il]
  • [www.cse.ucsc.edu]
  • [cs.ucsc.edu]
  • [www.cs.technion.ac.il]
  • [users.soe.ucsc.edu]
  • [www.soe.ucsc.edu]
  • [users.soe.ucsc.edu]
  • [users.soe.ucsc.edu]
  • [www.cse.ucsc.edu]

  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Sina Farsiu , Michael Elad , Peyman Milanfar
Citations:9 - 6 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Farsiu04multi-framedemosaicing,
    author = {Sina Farsiu and Michael Elad and Peyman Milanfar},
    title = {Multi-Frame Demosaicing and Super-Resolution from Under-Sampled Color Images},
    year = {2004}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

In the last two decades, two related categories of problems have been studied independently in the image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and as conventional color digital cameras suffer from both low-spatial resolution and color filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteriori (MAP) estimation technique by minimizing a multi-term cost function. The L 1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance component. Finally, an additional regularization term is used to force similar edge orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.

Keyphrases

under-sampled color image    multi-frame demosaicing    additional regularization term    unified context    multi-term cost function    projected estimate    robust hybrid method    image restoration literature    luminance component    tikhonov regularization    real data set    chrominance component    estimation technique    di erence    low-resolution image    related category    di erent color channel    high-resolution image    low-spatial resolution    total cost function    similar edge orientation    conventional color digital camera    bilateral regularization    sharp edge    motion estimation    color filtering    experimental result   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University