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Found 13 papers, 5 papers with code
Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
This is done by introducing new Gromov-type distances, designed to be isometry-invariant in Euclidean spaces and applicable for comparing GMMs across different dimensional spaces.
Properties of Discrete Sliced Wasserstein Losses
We investigate the regularity and optimisation properties of this energy, as well as its Monte-Carlo approximation $\mathcal{E}_p$ (estimating the expectation in SW using only $p$ samples) and show convergence results on the critical points of $\mathcal{E}_p$ to those of $\mathcal{E}$, as well as an almost-sure uniform convergence and a uniform Central Limit result on the process $\mathcal{E}_p(Y)$.
Wasserstein Dictionaries of Persistence Diagrams
This paper presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters [100], [102] of a dictionary of atom diagrams.
Video Restoration with a Deep Plug-and-Play Prior
Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme.
Can Push-forward Generative Models Fit Multimodal Distributions?
More precisely, we show that the total variation distance and the Kullback-Leibler divergence between the generated and the data distribution are bounded from below by a constant depending on the mode separation and the Lipschitz constant.
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution.
An Analysis and Implementation of the HDR+ Burst Denoising Method
HDR+ is an image processing pipeline presented by Google in 2016.
Wasserstein Distances, Geodesics and Barycenters of Merge Trees
We extend recent work on the edit distance [106] and introduce a new metric, called the Wasserstein distance between merge trees, which is purposely designed to enable efficient computations of geodesics and barycenters.
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
The proposed algorithms are demonstrated on several canonical problems such as image deblurring, inpainting, and denoising, where they are used for point estimation as well as for uncertainty visualisation and quantification.
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.
Ranked #5 on
Video Denoising
on Set8 sigma10
DVDnet: A Fast Network for Deep Video Denoising
Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods.
Ranked #5 on
Video Denoising
on DAVIS sigma30
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure.
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer
This paper presents a novel unsupervised method to transfer the style of an example image to a source image.
