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The camera onboard the European Space Agency's OPS-SAT-1 spacecraft has been operating past its design life. As the payload ages, there is an increasing risk of sensor degradation leading to corrupted images. This paper evaluates Generative AI models with Wasserstein GANs (WGANs)—an enhanced type of Generative Adversarial Networks—as a noise reduction solution to reconstruct noisy images directly onboard the spacecraft. Autoencoder neural networks are also trained and evaluated for comparative purposes given their common use in noise reduction. Images downlinked from the spacecraft serve as training data to which artificial fixed-pattern noise is applied to simulate sensor degradation. The trained neural networks are uplinked to the spacecraft's edge computer payload where they are processed by the onboard TensorFlow Lite interpreter to output the reconstructed images. On September 29, 2023, the OPS-SAT-1 mission achieved a significant milestone when it successfully captured, noised, and subsequently denoised two images using WGANs, marking the pioneering first application of Generative AI in space. The restored images have remarkably high structural similarity indices of 0.894 and 0.922—where 1 would indicate that they are identical to their original images. Interestingly, some reconstructed images are more confidently labeled by the onboard convolutional neural network image classifier than their original counterparts. The counterintuitive observation challenges the conventional understanding that higher resolution always yields better results. This suggests that simplifying or modifying certain data features enhances the ability of some models to accurately interpret given inputs.
Labrèche et al. (Sat,) studied this question.