Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery

Autor:	Lafenetre, Jamy, Nguyen, Ngoc Long, Facciolo, Gabriele, Eboli, Thomas
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Image resolution is an important criterion for many applications based on satellite imagery. In this work, we adapt a state-of-the-art kernel regression technique for smartphone camera burst super-resolution to satellites. This technique leverages the local structure of the image to optimally steer the fusion kernels, limiting blur in the final high-resolution prediction, denoising the image, and recovering details up to a zoom factor of 2. We extend this approach to the multi-exposure case to predict from a sequence of multi-exposure low-resolution frames a high-resolution and noise-free one. Experiments on both single and multi-exposure scenarios show the merits of the approach. Since the fusion is learning-free, the proposed method is ensured to not hallucinate details, which is crucial for many remote sensing applications. Comment: 9 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.05879 Zobrazit plný text záznamu View this record from Arxiv