Super-resolution consists in reconstructing a high-resolution image from single or multiple low-resolution observations. Deep learning has been reported extremely successful for single-image superresolution, but its applications to the multiple-image scenarios are limited due to the challenges that arise from feeding a network with a stack of images with sub-pixel translations. In this paper, we introduce Magnet-a new graph neural network that benefits from representing the input low-resolution images as a graph. This enables us to exploit the sub-pixel shifts among the input images while preserving the original low-resolution pixel values for feature extraction and information fusion. Despite a relatively simple architecture, Magnet outperforms the state-of-the-art methods for multiple-image superresolution, and due to the flexible graph representation, it allows for using a variable number of low-resolution images for reconstruction.
Insufficient spatial resolution of satellite imagery, including Sentinel-2 data, is a serious limitation in many practical use cases. To mitigate this problem, super-resolution reconstruction is receiving considerable attention from the remote sensing community. When it is performed from multiple images captured at subsequent revisits, it may benefit from information fusion, leading to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-life benchmark datasets-most of the research was performed for simulated data which do not fully reflect the operating conditions. In this letter, we introduce a new MuS2 benchmark for multi-image super-resolution reconstruction of Sentinel-2 images, with WorldView-2 imagery used as the highresolution reference. Within MuS2, we publish the first end-toend evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multiimage super-resolution for Sentinel-2 imagery.
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