Incorporating learned geospatial embeddings to deep image prior for inpainting cloud areas in remotely sensed images
Monday April 6, 2026
Incorporating learned geospatial embeddings to deep image prior for inpainting cloud areas in remotely sensed images Cloud removal in optical remote sensing typically relies on multi-temporal or multi-source data and large-scale supervised training, which limits its applicability in data-scarce scenarios. Although the deep image prior (DIP), an internal learning regime, enables training-free reconstruction from a […]
