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Communication Dans Un Congrès Année : 2021

Self-supervised multi-image super-resolution for push-frame satellite images

Résumé

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR) from bursts of push-frame images as a way to increase the resolution and reduce the noise of their products while maintaining a lower cost of operation. Most MISR techniques are currently based on the aggregation of samples from registered low resolution images. A promising research trend aimed at incorporating natural image priors in MISR consists in using data-driven neural networks. However, due to the unavailability of ground truth high resolution data, these networks cannot be trained on real satellite images. In this paper, we present a framework for training MISR algorithms from bursts of satellite images without requiring high resolution ground truth. This is achieved by adapting the recently proposed frame-to-frame framework to process bursts of satellite images. In addition we propose an architecture based on feature aggregation that allows to fuse a variable number of frames and is capable of handling degenerate samplings while also reducing noise. On synthetic datasets, the proposed self-supervision strategy attains results on par with those obtained with a supervised training. We applied our framework to real SkySat satellite image bursts leading to results that are more resolved and less noisy than the L1B product from Planet
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Dates et versions

hal-04497742 , version 1 (10-03-2024)

Identifiants

Citer

Ngoc Long Nguyen, Jeremy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo. Self-supervised multi-image super-resolution for push-frame satellite images. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2021), Jun 2021, Nashville, TN, United States. pp.1121-1131, ⟨10.1109/CVPRW53098.2021.00123⟩. ⟨hal-04497742⟩
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