# Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites

arXiv: https://arxiv.org/abs/2205.02031 · HTML: https://mlsanity.com/paper/2205.02031/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Frame-Recurrent Video Super-Resolution](https://mlsanity.com/paper/1801.04590/) (2018)
- [Detail-revealing Deep Video Super-resolution](https://mlsanity.com/paper/1704.02738/) (2017)
- [SelFlow: Self-Supervised Learning of Optical Flow](https://mlsanity.com/paper/1904.09117/) (2019)
- [Video Super-Resolution with Recurrent Structure-Detail Network](https://mlsanity.com/paper/2008.00455/) (2020)
- [Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness](https://mlsanity.com/paper/1608.05842/) (2016)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Deep Burst Super-Resolution](https://mlsanity.com/paper/2101.10997/) (2021)
- [Unsupervised Deep Video Denoising](https://mlsanity.com/paper/2011.15045/) (2020)
- [HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery](https://mlsanity.com/paper/2002.06460/) (2020)
- [Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications](https://mlsanity.com/paper/1904.11176/) (2019)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video](https://mlsanity.com/paper/1909.04391/) (2019)
- [DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images](https://mlsanity.com/paper/1907.06490/) (2019)
- [Self-Supervised training for blind multi-frame video denoising](https://mlsanity.com/paper/2004.06957/) (2020)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2104.06191/) (2021)
- [Super-Resolution of PROBA-V Images Using Convolutional Neural Networks](https://mlsanity.com/paper/1907.01821/) (2019)
- [A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging](https://mlsanity.com/paper/1706.03261/) (2017)
- [DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images](https://mlsanity.com/paper/2001.06342/) (2020)
- [Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw Images](https://mlsanity.com/paper/1905.05092/) (2019)
- [HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models](https://mlsanity.com/paper/2012.12009/) (2020)
- [Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution](https://mlsanity.com/paper/2101.10200/) (2021)
- [Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products](https://mlsanity.com/paper/2102.02301/) (2021)

## Built on by (most cited first, 8 of 8)

- [Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery](https://mlsanity.com/paper/2303.05879/) (2023)
- [Perspective-Equivariance for Unsupervised Imaging with Camera Geometry](https://mlsanity.com/paper/2403.09327/) (2024)
- [L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery](https://mlsanity.com/paper/2304.06871/) (2023)
- [EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation](https://mlsanity.com/paper/2503.11371/) (2025)
- [SuperF: Neural Implicit Fields for Multi-Image Super-Resolution](https://mlsanity.com/paper/2512.09115/) (2025)
- [The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks](https://mlsanity.com/paper/2211.05018/) (2022)
- [Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images](https://mlsanity.com/paper/2603.14074/) (2026)
- [Enhanced Self-Supervised Multi-Image Super-Resolution for Camera Array Images](https://mlsanity.com/paper/2604.06816/) (2026)

## Abstract (arXiv)

Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resolution method for such multi-exposure sequences, a problem that has received very little attention in the literature. The proposed method can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times. Furthermore, it can be trained end-to-end with self-supervision, without requiring ground truth high resolution frames, which makes it especially suited to handle real data. Central to our method are three key contributions: i) a base-detail decomposition for handling errors in the exposure times, ii) a noise-level-aware feature encoding for improved fusion of frames with varying signal-to-noise ratio and iii) a permutation invariant fusion strategy by temporal pooling operators. We evaluate the proposed method on synthetic and real data and show that it outperforms by a significant margin existing single-exposure approaches that we adapted to the multi-exposure case.