Spacecraft image super-resolution seeks to enhance low-resolution spacecraft images into high-resolution ones. Although existing arbitrary-scale super-resolution methods perform well on general images, they tend to overlook the difference in features between the spacecraft core region and the large black space background, introducing irrelevant noise. In this paper, we propose a salient region-guided spacecraft image arbitrary-scale super-resolution network (SGSASR), which uses features from the spacecraft core salient regions to guide latent modulation and achieve arbitrary-scale super-resolution. Specifically, we design a spacecraft core region recognition block (SCRRB) that identifies the core salient regions in spacecraft images using a pre-trained saliency detection model. Furthermore, we present an adaptive-weighted feature fusion enhancement mechanism (AFFEM) to selectively aggregate the spacecraft core region features with general image features by dynamic weight parameter to enhance the response of the core salient regions. Experimental results demonstrate that the proposed SGSASR outperforms state-of-the-art approaches.

翻译：航天器图像超分辨率旨在将低分辨率航天器图像增强为高分辨率图像。尽管现有的任意尺度超分辨率方法在通用图像上表现良好，但它们往往忽略了航天器核心区域与广阔黑色空间背景之间的特征差异，从而引入了无关噪声。本文提出一种显著区域引导的航天器图像任意尺度超分辨率网络（SGSASR），该网络利用航天器核心显著区域的特征进行潜在调制，实现任意尺度超分辨率。具体而言，我们设计了航天器核心区域识别模块（SCRRB），通过预训练的显著性检测模型识别航天器图像中的核心显著区域。此外，我们提出自适应加权特征融合增强机制（AFFEM），通过动态权重参数选择性地聚合航天器核心区域特征与通用图像特征，以增强核心显著区域的响应。实验结果表明，所提出的SGSASR方法优于现有先进方法。