MathImag's repositories
2019-TCI-OnlinePnP
The code for "An online plug-and-play algorithm for regularized image reconstruction", IEEE TCI, 2019.
CURE-Curvature-Regularization-Via-Biharmonic-Extension
Code for "CUBE: Curvature Regularization Via Weighted Nonlocal BiHarmonic"
DeepRED
DeepRED: Deep Image Prior Powered by RED
fast-Non-local-Means-and-Asymptotic-Non-local-Means
Non-Local means denoising (NLM) algorithm is a milestone algorithm in the field of image processing. The proposal of NLM has opened up the non-local method which has a deep influence. This paper performed a revisit for NLM from two aspects as follows: 1. To alleviate the high computational complexity problem of NLM, a fast algorithm was constructed, which was based on cross-correlation and fast Fourier transform; 2. NLM always blur structures and textures during the noise removal, especially in the case of strong noise. To solve this problem, an Asymptotic Non-Local Means image denoising algorithm is put forward, which uses the property of noise variance to control the filtering parameters. Numerical experiments illustrate that the fast algorithm is 27 times faster than classical implementation with standard parameter configuration, and the ANLM uniformly outperforms classical NLM, in terms of both PSNR and visual effects.
FastHDFilter
P. Nair, and K. N. Chaudhury, "Fast High-Dimensional Bilateral and Non-local Means Filtering," , IEEE Transactions on Image Processing, 28(3), pp.1470-1481.
FastNonlocalDenoising
. S. Ghosh and K. N. Chaudhury, “Fast separable non-local means,” Journal of Electronic Imaging, vol. 25, no. 2, pp. 023026: 1- 14, 2016.
FIMA
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems (TPAMI 2019)
HNHOTV_OGS
Hybrid higher order non-convex total variation with overlapping group sparsity image denoising
Image-Signal-Processing
Collection of inverse problems in image processing via Alternating Direction Method (ADM), Alternating Minimization Method (AMA), Group Sparse signal denoising and Majorization-Minimization optimization based image/signal processing
Iterative-Wiener-Filter-Super-resolution
Kwok-Wai Hung, *Wan-Chi Siu, Single-Image Super-Resolution Using Iterative Wiener Filter based on Nonlocal Means, Signal Processing: Image Communication, Vol. 39, Part A, pp 26-45, November 2015.
madgrad
MADGRAD Optimization Method
mathematical-tours.github.io
Site web of the Mathematical Tours
matImage
Image Processing library for Matlab
MCWNNM-ICCV2017
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising, ICCV 2017.
Non-blind-and-Blind-Deconvolution-under-Poisson-noise
Non-blind and Blind Deconvolution under Poisson noise via Fractional-order Total Variation
Optimized-Bayesian-Nonlocal-means-with-block-OBNLM
Optimized bayesian nonlocal-means algorithm for denoising ultrasound image
PRMLT
Matlab code for machine learning algorithms in book PRML
reproducible-image-denoising-state-of-the-art
Collection of popular and reproducible image denoising works.
research-code
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems
Saliency-guided-image-enhancement
S. Ghosh, R. G. Gavaskar, and K. N. Chaudhury, “Saliency guided image detail enhancement,” Proc. National Conference on Communications (NCC), Bangalore, India, 2019.
SideWindowFilter
Side window is better than Full window
SNN
Matlab code implementation the modified Non Local Means and Bilateral filters, as described in I. Frosio, J. Kautz, Statistical Nearest Neighbors for Image Denoising, IEEE Trans. Image Processing, 2018. The repository also includes the Matlab code to replicate the results of the toy problem described in the paper.
Speckle-Reducing-Anisotropic-Diffusion-SRAD
Speckle Reducing Anisotropic Diffusion (SRAD) Algorithm
TextureSmoothing
Fast Scale-Adaptive Bilateral Texture Smoothing, IEEE Transactions on Circuits and Systems for Video Technology, 2019.
TFPnP
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems (ICML 2020 Award Paper & JMLR 2022)
WeightedMeanCurvature
Weighted Mean Curvature is a better regularization than Total Variation