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Found 8 papers, 2 papers with code
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models.
Nonlocal Co-occurrence for Image Downscaling
In this work, we present a new downscaling technique which is based on kernel-based image filtering concept.
Linearized ADMM and Fast Nonlocal Denoising for Efficient Plug-and-Play Restoration
In plug-and-play image restoration, the regularization is performed using powerful denoisers such as nonlocal means (NLM) or BM3D.
Artifact reduction for separable non-local means
To bypass this, the authors proposed a separable approximation in which the image rows and columns are filtered using lifting.
Pruned non-local means
In Non-Local Means (NLM), each pixel is denoised by performing a weighted averaging of its neighboring pixels, where the weights are computed using image patches.
Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation
A direct implementation of the Gaussian bilateral filter requires $O(\sigma_s^2)$ operations per pixel, where $\sigma_s$ is the standard deviation of the spatial Gaussian.
Fast Bilateral Filtering of Vector-Valued Images
In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images.
On Fast Bilateral Filtering using Fourier Kernels
By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy.
