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Found 18 papers, 3 papers with code
Plug-and-play ISTA converges with kernel denoisers
Plug-and-play (PnP) method is a recent paradigm for image regularization, where the proximal operator (associated with some given regularizer) in an iterative algorithm is replaced with a powerful denoiser.
On the Proof of Fixed-Point Convergence for Plug-and-Play ADMM
We argue that the original proof is incomplete, since convergence is not analyzed for one of the three possible cases outlined in the paper.
Fast High-Dimensional Kernel Filtering
We demonstrate the effectiveness of our proposal for bilateral and nonlocal means filtering of color and hyperspectral images.
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.
Fast Adaptive Bilateral Filtering
In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing.
Fast High-Dimensional Bilateral and Nonlocal Means Filtering
Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data.
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.
Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval
Different scenarios of cross-modal matching are possible, for example, data from the different modalities can be associated with a single label or multiple labels, and in addition may or may not have one-to-one correspondence.
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 Bilateral Filtering of Vector-Valued Images
In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images.
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 and Provably Accurate Bilateral Filtering
The bilateral filter is a non-linear filter that uses a range filter along with a spatial filter to perform edge-preserving smoothing of 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.
Fast and Accurate Bilateral Filtering using Gauss-Polynomial Decomposition
A widely-used form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian.
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach
Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.
A new ADMM algorithm for the Euclidean median and its application to robust patch regression
The Euclidean Median (EM) of a set of points $\Omega$ in an Euclidean space is the point x minimizing the (weighted) sum of the Euclidean distances of x to the points in $\Omega$.
Global registration of multiple point clouds using semidefinite programming
We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the semidefinite program (i. e., we are able to solve the original non-convex least-squares problem) up to a certain noise threshold, and (b) the semidefinite program performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.
On the convergence of the IRLS algorithm in Non-Local Patch Regression
[CS2013] Chaudhury et al. (2013), "Non-local patch regression: Robust image denoising in patch space," IEEE ICASSP.
