Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images

• URL: http://arxiv.org/abs/2603.14074v1
• Date: Sat, 14 Mar 2026 18:41:43 GMT
• Title: Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images
• Abstract summary: Super-resolution satellite imagery is challenging due to the lack of paired low-/high-resolution data.<n>Recent self-supervised SR methods overcome this limitation by exploiting the temporal redundancy in burst observations.<n>We introduce a novel self-supervised loss that allows to estimate uncertainty in image super-resolution without ever accessing the ground-truth high-resolution data.
• Score: 6.86762910763467
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Super-resolution (SR) of satellite imagery is challenging due to the lack of paired low-/high-resolution data. Recent self-supervised SR methods overcome this limitation by exploiting the temporal redundancy in burst observations, but they lack a mechanism to quantify uncertainty in the reconstruction. In this work, we introduce a novel self-supervised loss that allows to estimate uncertainty in image super-resolution without ever accessing the ground-truth high-resolution data. We adopt a decision-theoretic perspective and show that minimizing the corresponding Bayesian risk yields the posterior mean and variance as optimal estimators. We validate our approach on a synthetic SkySat L1B dataset and demonstrate that it produces calibrated uncertainty estimates comparable to supervised methods. Our work bridges self-supervised restoration with uncertainty quantification, making a practical framework for uncertainty-aware image reconstruction.

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