解决空间站拍摄图像的模糊问题，实现实时无参考去模糊。
Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach
- 基于GAN框架估计模糊核，无需参考图像即可恢复
- 合成数据上SSIM提升72.47%，PSNR提升25.00%
- 已在IMAGIN-e任务中部署，适合资源受限的星载计算
本文针对国际空间站搭载的IMAGIN-e任务中地球观测图像的机械性失焦问题，提出一种适配星载边缘计算约束的盲去模糊方法。利用Sentinel-2数据，该方法在无参考图像条件下，通过生成对抗网络框架估计失焦核并训练复原模型。在含合成退化的Sentinel-2图像上，结构相似性（SSIM）提升72.47%，峰值信噪比（PSNR）提升25.00%，验证了在已知原始清晰图像时的细节恢复能力。在真实IMAGIN-e数据上，因无参考图像，采用感知质量指标评估：NIQE下降60.66%，BRISQUE下降48.38%，表明图像质量显著提升。该方法已实际部署于IMAGIN-e任务，可在高分辨率图像与资源受限条件下，支持水体分割、轮廓检测等应用，具备星载实时处理可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。