Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach
It addresses image quality issues for Earth observation applications in space missions, enabling better data analysis under resource constraints, but is incremental as it adapts existing methods to a specific domain.
This work tackled mechanical defocus blur in Earth observation images from the IMAGIN-e mission on the ISS by proposing a blind deblurring approach adapted to space-based edge computing constraints, achieving improvements like 72.47% in SSIM and 25.00% in PSNR on synthetic data, and 60.66% in NIQE and 48.38% in BRISQUE on real mission data.
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.