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Found 5 papers, 0 papers with code
Robust Matrix Regression
However, in real-world applications, it is common to see the training data contaminated by noises, which can affect the robustness of these matrix regression methods.
From Noise Modeling to Blind Image Denoising
This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even when the noise model is not provided.
Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
We further develop a novel online learning approach for Variational inference and use it for the online learning of HeMF, which can efficiently cope with the important large-scale DDP problem.
Blind Image Denoising via Dependent Dirichlet Process Tree
This paper addresses this problem and proposes a novel blind image denoising algorithm to recover the clean image from noisy one with the unknown noise model.
An Efficient Statistical Method for Image Noise Level Estimation
In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise.
