PROBA-V-REF: Repurposing the PROBA-V Challenge for Reference-Aware Super Resolution
Ngoc Long Nguyen,Jérémy Anger,Axel Davy,Pablo Arias,Gabriele Facciolo +4 more
- pp 3881-3884
TLDR
The authors The authors is a variant of the PROBA-V dataset, in which the reference image in the low-resolution series is provided, and show that the ranking between the methods changes in this setting.Abstract:
The PROBA-V Super-Resolution challenge distributes real low-resolution image series and corresponding high-resolution targets to advance research on Multi-Image Super Resolution (MISR) for satellite images. However, in the PROBA-V dataset the low-resolution image corresponding to the high-resolution target is not identified. We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target. We demonstrate this by improving the performance obtained by the two winners of the challenge only by using a different reference image, which we compute following a simple heuristic. Based on this, we propose PROBA-V-REF a variant of the PROBA-V dataset, in which the reference image in the low-resolution series is provided, and show that the ranking between the methods changes in this setting. This is relevant to many practical use cases of MISR where the goal is to super-resolve a specific image of the series, i.e. the reference is known. The proposed PROBA-V-REF should better reflect the performance of the different methods for this reference-aware MISR problem.read more
Citations
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Proceedings ArticleDOI
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites
TL;DR: This work proposes a super-resolution method that can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times, and can be trained end-to-end with self-supervision, which makes it especially suited to handle real data.
Proceedings ArticleDOI
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites
TL;DR: In this article , the authors proposed a super-resolution method for multi-exposure bursts of push-frame images that can be super-resolved via computational means, which can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times.
Journal ArticleDOI
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
Tomasz Tarasiewicz,Jakub Nalepa,Reuben A. Farrugia,Gianluca Valentino,Mang Chen,Johann A. Briffa,Michal Kawulok +6 more
TL;DR: DeepSent as mentioned in this paper is a new deep network for super-resolving multitemporal series of multispectral Sentinel-2 images, which is underpinned with information fusion per-formed simultaneously in the spectral and temporal dimensions to generate an enlarged multi-spectral image.
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