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Spacecraft image super-resolution seeks to enhance low-resolution spacecraftimages into high-resolution ones. Although existing arbitrary-scalesuper-resolution methods perform well on general images, they tend to overlookthe difference in features between the spacecraft core region and the largeblack space background, introducing irrelevant noise. In this paper, we proposea salient region-guided spacecraft image arbitrary-scale super-resolutionnetwork (SGSASR), which uses features from the spacecraft core salient regionsto guide latent modulation and achieve arbitrary-scale super-resolution.Specifically, we design a spacecraft core region recognition block (SCRRB) thatidentifies the core salient regions in spacecraft images using a pre-trainedsaliency detection model. Furthermore, we present an adaptive-weighted featurefusion enhancement mechanism (AFFEM) to selectively aggregate the spacecraftcore region features with general image features by dynamic weight parameter toenhance the response of the core salient regions. Experimental resultsdemonstrate that the proposed SGSASR outperforms state-of-the-art approaches.

Salient Region-Guided Spacecraft Image Arbitrary-Scale Super-Resolution Network