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Found 3 papers, 1 papers with code
CODA: Counting Objects via Scale-aware Adversarial Density Adaption
Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models.
Learning Parametric Sparse Models for Image Super-Resolution
More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding
Existing approaches toward Image super-resolution (SR) is often either data-driven (e. g., based on internet-scale matching and web image retrieval) or model-based (e. g., formulated as an Maximizing a Posterior estimation problem).
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