Spacecraft image denoising is a crucial fundamental technology closely related to aerospace research. However, the existing deep learning-based image denoising methods are primarily designed for natural image and fail to adequately consider the characteristics of spacecraft image(e.g. low-light conditions, repetitive periodic structures), resulting in suboptimal performance in the spacecraft image denoising task. To address the aforementioned problems, we propose a Structure modeling Activation Free Fourier Network (SAFFN), which is an efficient spacecraft image denoising method including Structure Modeling Block (SMB) and Activation Free Fourier Block (AFFB). We present SMB to effectively extract edge information and model the structure for better identification of spacecraft components from dark regions in spacecraft noise image. We present AFFB and utilize an improved Fast Fourier block to extract repetitive periodic features and long-range information in noisy spacecraft image. Extensive experimental results demonstrate that our SAFFN performs competitively compared to the state-of-the-art methods on spacecraft noise image datasets. The codes are available at: https://github.com/shenduke/SAFFN.

翻译：航天器图像去噪是一项与航空航天研究密切相关的关键基础技术。然而，现有的基于深度学习的图像去噪方法主要针对自然图像设计，未能充分考虑航天器图像的特性（例如低光照条件、重复的周期性结构），导致在航天器图像去噪任务中性能欠佳。为解决上述问题，我们提出了一种结构建模无激活傅里叶网络（SAFFN），这是一种高效的航天器图像去噪方法，包含结构建模模块（SMB）和无激活傅里叶模块（AFFB）。我们提出的SMB能有效提取边缘信息并建模结构，以更好地从航天器噪声图像的暗区中识别航天器组件。我们提出的AFFB利用改进的快速傅里叶块来提取噪声航天器图像中的重复周期性特征和长程信息。大量实验结果表明，我们的SAFFN在航天器噪声图像数据集上相比现有最先进方法具有竞争力。代码发布于：https://github.com/shenduke/SAFFN。