针对航天图像噪声问题，提出无需激活函数的傅里叶网络，有效提升暗光下结构识别能力。
Structure Modeling Activation Free Fourier Network for Spacecraft Image Denoising
- 设计结构建模块与无激活傅里叶块，分别提取边缘结构与周期性特征。
- 在航天图像数据集上优于当前最优方法，显著提升暗区组件识别效果。
- 适合低光、含周期结构的航天图像去噪任务，代码开源可复现。
航天图像去噪是航空航天研究中的关键技术。现有基于深度学习的去噪方法多针对自然图像设计，未能充分考虑航天图像的特性（如低光照、重复周期结构），导致性能不佳。为此，本文提出结构建模无激活傅里叶网络（SAFFN），包含结构建模块（SMB）和无激活傅里叶块（AFFB）。SMB用于有效提取边缘信息并建模结构，以更好识别暗区中的航天器部件；AFFB通过改进的快速傅里叶变换模块，提取噪声图像中的重复周期特征与长程依赖关系。大量实验表明，SAFFN在航天图像噪声数据集上表现优于当前先进方法。代码已公开于：https://github.com/shenduke/SAFFN。
原文摘要 · Abstract (English)
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.
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