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Self-Supervised Push-Frame Super-Resolution With Detail-Preserving Control And Outlier Detection

Self-supervised training enables the application of deep-learning based methods for multi-image super-resolution of satellite imagery. In this work we propose two improvements on the self-supervised Deep-Shift-and-Add (DSA) method introduced by Nguyen et al. First, we demonstrate how the self-supervised loss of DSA can be extended to provide the image interpreter with a spatially varying parameter to control the trade-off between detail preservation and noise removal at test time. Second, we endow the DSA architecture with a mechanism that enables the network to be robust to outliers produced for example by dead pixels, reflections or registration errors.