J
John Paisley
Researcher at Columbia University
Publications - 146
Citations - 11369
John Paisley is an academic researcher from Columbia University. The author has contributed to research in topics: Inference & Compressed sensing. The author has an hindex of 39, co-authored 137 publications receiving 8501 citations. Previous affiliations of John Paisley include Princeton University & Duke University.
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Stochastic variational inference
TL;DR: Stochastic variational inference lets us apply complex Bayesian models to massive data sets, and it is shown that the Bayesian nonparametric topic model outperforms its parametric counterpart.
Proceedings ArticleDOI
Removing Rain from Single Images via a Deep Detail Network
TL;DR: A deep detail network is proposed to directly reduce the mapping range from input to output, which makes the learning process easier and significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures.
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Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
TL;DR: Zhang et al. as mentioned in this paper introduced a deep network architecture called DerainNet for removing rain streaks from an image, which directly learned the mapping relationship between rainy and clean image detail layers from data.
Proceedings ArticleDOI
PanNet: A Deep Network Architecture for Pan-Sharpening
TL;DR: This work incorporates domain-specific knowledge to design the PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation, and shows that the trained network generalizes well to images from different satellites without needing retraining.
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A fusion-based enhancing method for weakly illuminated images
TL;DR: A fusion-based method for enhancing various weakly illuminated images that requires only one input to obtain the enhanced image and represents a trade-off among detail enhancement, local contrast improvement and preserving the natural feel of the image.