GPU-Based Edge-Directed Image Interpolation

  • Martin Kraus
  • Mike Eissele
  • Magnus Strengert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)

Abstract

The rendering of lower resolution image data on higher resolution displays has become a very common task, in particular because of the increasing popularity of webcams, camera phones, and low-bandwidth video streaming. Thus, there is a strong demand for real-time, high-quality image magnification. In this work, we suggest to exploit the high performance of programmable graphics processing units (GPUs) for an adaptive image magnification method. To this end, we propose a GPU-friendly algorithm for image up-sampling by edge-directed image interpolation, which avoids ringing artifacts, excessive blurring, and staircasing of oblique edges. At the same time it features gray-scale invariance, is applicable to color images, and allows for real-time processing of full-screen images on today’s GPUs.

Keywords

Subdivision Scheme Camera Phone Image Interpolation Ideal Edge Edge Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Martin Kraus
    • 1
  • Mike Eissele
    • 2
  • Magnus Strengert
    • 2
  1. 1.Computer Graphics and Visualization Group, Informatik 15, Technische Universität München, Boltzmannstraße 3, 85748 GarchingGermany
  2. 2.Visualization and Interactive Systems Group, Institut VIS, Universität Stuttgart, Universitätsstraße 38, 70569 StuttgartGermany

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