Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

TL;DR

This paper presents a real-time, blind deblurring method for Earth observation images from space, using GANs to restore images affected by mechanical defocus without reference images, suitable for onboard edge computing constraints.

Contribution

The paper introduces a novel blind deblurring approach tailored for space-based Earth observation, leveraging GANs and synthetic data for effective onboard image restoration.

Findings

Significant SSIM and PSNR improvements on Sentinel-2 data.

Enhanced perceptual quality metrics on IMAGIN-e images.

Successful deployment of the method aboard the IMAGIN-e mission.

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.