针对航天器图像超分辨，用关键区域引导提升任意缩放效果。
Towards Arbitrary-Scale Spacecraft Image Super-Resolution via Salient Region-Guidance
- 通过识别航天器核心区域，指导特征调制。
- 在多个缩放倍数下均优于现有方法，尤其在细节还原上表现突出。
- 适合需要高精度航天图像处理的科研与工程场景。
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，但往往忽略航天器核心区域与大面积黑色背景之间的特征差异，引入无关噪声。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），利用航天器核心显著区域的特征引导潜在空间调制，实现任意尺度超分辨率。具体而言，设计了航天器核心区域识别模块（SCRRB），通过预训练显著性检测模型识别航天器图像中的核心显著区域；此外，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聚合核心区域特征与通用图像特征，增强对核心显著区域的响应。实验结果表明，所提SGSASR优于当前最优方法。
原文摘要 · Abstract (English)
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.
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