Abstract
This work addresses mechanical defocus in Earth observation images from the IMAGIN-e mission aboard the ISS, proposing a blind deblurring approach adapted to space-based edge computing constraints. Leveraging Sentinel-2 data, our method estimates the defocus kernel and trains a restoration model within a GAN framework, effectively operating without reference images. On Sentinel-2 images with synthetic degradation, SSIM improved by 72.47% and PSNR by 25.00%, confirming the model's ability to recover lost details when the original clean image is known. On IMAGIN-e, where no reference images exist, perceptual quality metrics indicate a substantial enhancement, with NIQE improving by 60.66% and BRISQUE by 48.38%, validating real-world onboard restoration. The approach is currently deployed aboard the IMAGIN-e mission, demonstrating its practical application in an operational space environment. By efficiently handling high-resolution images under edge computing constraints, the method enables applications such as water body segmentation and contour detection while maintaining processing viability despite resource limitations.
Abstract (translated)
这项工作解决了国际空间站（ISS）上IMAGIN-e任务地球观测图像中的机械失焦问题，提出了一种适应基于太空边缘计算限制的盲去模糊方法。通过利用Sentinel-2数据，我们的方法估计了失焦核，并在一个生成对抗网络（GAN）框架内训练了一个恢复模型，能够在没有参考图像的情况下有效运行。在具有合成退化处理的Sentinel-2图像上，SSIM（结构相似性指数）提高了72.47%，PSNR（峰值信噪比）提高了25.00%，这证实了该模型能够恢复已知原始清晰图像时丢失的细节。在没有参考图像存在的IMAGIN-e任务中，感知质量度量表明有了显著提升：NIQE（自然图像质量评价器）提高了60.66%，BRISQUE（基于回归的无监督图像质量评估）提高了48.38%，这验证了其在实际环境中的恢复能力。该方法目前部署于IMAGIN-e任务中，展示了其在操作性太空环境中应用的实际价值。 通过高效处理边缘计算限制下的高分辨率图像，该方法使诸如水体分割和轮廓检测等应用程序得以实现，并且即使在资源受限的情况下也能保持处理的可行性。
URL
https://arxiv.org/abs/2505.22128
