Exploiting Redundancy for Aerial Image Fusion Using Convex Optimization

  • Stefan Kluckner
  • Thomas Pock
  • Horst Bischof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)


Image fusion in high-resolution aerial imagery poses a challenging problem due to fine details and complex textures. In particular, color image fusion by using virtual orthographic cameras offers a common representation of overlapping yet perspective aerial images. This paper proposes a variational formulation for a tight integration of redundant image data showing urban environments. We introduce an efficient wavelet regularization which enables a natural-appearing recovery of fine details in the images by performing joint inpainting and denoising from a given set of input observations. Our framework is first evaluated on a setting with synthetic noise. Then, we apply our proposed approach to orthographic image generation in aerial imagery. In addition, we discuss an exemplar-based inpainting technique for an integrated removal of non-stationary objects like cars.


Image Fusion Convex Optimization Impulse Noise Aerial Image Fusion Model 
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-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stefan Kluckner
    • 1
  • Thomas Pock
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria

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