PEFD recovers fine spectral details without ground truth by exploiting projective geometry and adapting foundation models, nearing supervised performance on surgical and automotive data.
Self-Supervised Learning for Image Super- Resolution and Deblurring
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
citing papers explorer
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Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth
PEFD recovers fine spectral details without ground truth by exploiting projective geometry and adapting foundation models, nearing supervised performance on surgical and automotive data.
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Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.