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Paper data
A minimax-constrained superresolution algorithm for remote sensing imagery

Magli Enrico, Dip. di Elettronica - Politecnico di Torino
Olmo Gabriella, Dip. di Elettronica - Politecnico di Torino

Page numbers in the proceedings:
Volume II pp 453-456

Image Restoration

Paper abstract
Superresolution algorithms use several blurred, undersampled and noisy images of a scene to reconstruct a higher resolution version. In this paper we apply the superresolution concept to the remote sensing scenario, and develop a novel superresolution algorithm based on quadratic programming, and compare it with existing methods. The proposed algorithm achieves PSNR performance similar to state-of-the-art techniques, providing additional capabilities in terms of uniqueness of the solution and user-defined bounds for the superresolution problem.

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