Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)
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Updated
Oct 9, 2021 - MATLAB
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)
This repository contains MATLAB scripts and sample seismic data for appying seismid denoising proposed in: "Hybrid Seismic Denoising Using Higher‐Order Statistics and Improved Wavelet Block Thresholding"
Source code of "A Single Model CNN for Hyperspectral Image Denoising"
ASPP: Binaural Speech Enhancement with Atomic Speech Presence Probability Estimation
Panoramic robust PCA for foreground-background separation on noisy, free-motion camera video
Compressed sensing and denoising of images using sparse representations
Noise-level estimation using minima controlled recursive averaging approach and denoising using Stein's unbiased risk estimates in STFT domain.
A Low-rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising.
This packaged is an implementation of our paper "Robust Denoising of Piece-Wise Smooth Manifolds", ICASSP 2018 The algorithm creates an affinity graph and perform denoising on a set of N input points in R^n. Given an input set of points in any arbitrary dimension, an affinity graph is first created based on Tensor Voting, Local PCA or Euclidean …
Covariance Estimation and Denoising for Cryo-EM Images (Covariance Wiener Filtering)
This is a very simple denoising code for seismic data. It contains two different basic thresholding functions and works in continuous wavelet domain.
codes for TNNLS paper "Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration"
MATLAB implementation of sparsity-assisted signal denoising and pattern recognition in time-series data
This repository contains MATLAB scripts and sample data for applying denoising method presented in: "Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data"
This is a point cloud denoiser using Weighted Locally Optimal Projection
Automatic Microseismic Denoising and Onset Detection using customized thresholding.
Low-rank matrix estimation using convex non-convex prior
Signal and image denoising using quantum adaptive transformation.
OCTOBOS, overcomplete transform, learning and application codes, Matlab implementation, IJCV2015 paper
This is a reverse algorithem for GCV method that removes the signal and keep the background noise
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