Structure Modeling Activation Free Fourier Network for Spacecraft Image Denoising

TL;DR

This paper introduces SAFFN, a novel Fourier-based neural network designed specifically for denoising spacecraft images by modeling structural and periodic features, outperforming existing methods in challenging low-light conditions.

Contribution

The paper proposes SAFFN, a new neural network architecture with Structure Modeling Block and Activation Free Fourier Block tailored for spacecraft image denoising, addressing unique challenges of the domain.

Findings

SAFFN achieves competitive denoising performance on spacecraft datasets.

The method effectively captures structural and periodic features in noisy images.

Experimental results outperform state-of-the-art denoising techniques.

Abstract

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.