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• W4362063345 abstract "The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model. In this paper, we propose a novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner. CDCR consists of two components: a CR-based dense prediction that gathers more available information and a DR-based resolution-specific refinement that adjusts the predicted values of local pixels. Furthermore, we introduce a scale cumulative ascent (SCA) method, which enhances the performance of the dense prediction and improves the accuracy of the generated images at ultra-high magnifications. The efficacy and dependability of CDCR are substantiated by extensive experiments conducted on multiple remote sensing datasets, providing strong support for scenarios that require accurate images." @default.
• W4362063345 created "2023-04-05" @default.
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• W4362063345 date "2023-03-29" @default.
• W4362063345 modified "2023-09-26" @default.
• W4362063345 title "Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images" @default.
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• W4362063345 doi "https://doi.org/10.3390/rs15071827" @default.
• W4362063345 hasPublicationYear "2023" @default.
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