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Missing data reconstruction (e.g., dead pixel repair and cloud removing) in remote sensing images is a very important problem for the subsequent image analysis. It is well-known that missing data reconstruction is an ill-posed problem. In remote sensing images, there is a strong correlation in spectral frequencies or in temporal frames, and also there are a lot of self-similarity patterns in spatial...
As well known, Gaussian process regression (GPR) has been successfully applied to example learning-based image super-resolution (SR). Despite its effectiveness, the applicability of a GPR model is limited by its remarkably computational cost when a large number of examples are available to a learning task. For this purpose, we alleviate this problem of the GPR-based SR and propose a novel example...
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