Abstract
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.
This work was financed by the Austrian Research Promotion Agency within the projects vdQA (No. 816003) and APAFA (No. 813397).
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Kluckner, S., Pock, T., Bischof, H. (2010). Exploiting Redundancy for Aerial Image Fusion Using Convex Optimization. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_31
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DOI: https://doi.org/10.1007/978-3-642-15986-2_31
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