Cherylsu's starred repositories
Blind_Deconvolution
A Robust Blind Deconvolution Algorithm for Image Deblurring
TDV-for-image-denoising
This is a companion software for the submission: "Higher-Order Total Directional Variation: Imaging Applications" by Simone Parisotto , Jan Lellmann, Simon Masnou, and Carola-Bibiane Schönlieb. SIAM J. Imaging Sci., 13(4), 2063–2104. (42 pages)
fundus-vessel-segmentation-tbme
In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.
blind_remote_sensing
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
Hyperspectral-Image-Restoration-via-Total-Variation-Regularized-Low-rank-Tensor-Decomposition
code of Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Split-Bregman-ST-Total-Variation-MRI
Split Bregman spatiotemporal total variation for MRI
Weighted-Nonlocal-Total-Variation-in-Image-Processing
Weighted Nonlocal Total Variation in Image Processing
ADMM-DIPTV
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM (2021)
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
frPCA_sparse
fast randomized PCA for sparse data
matrix-completion
An ADMM + Compressed Sensing algorithm to estimate a low-rank sparse matrix
ADMM-Total-Variation
Some other ADMM total variation codes
aaai2016_changkyu
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty (AAAI 2016) - Changkyu Song
SparseGDLibrary
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
D-ADMM-code
Fast Total-Variation Based Image Restoration Based on Derivative Alternated Direction Optimization Methods
LibADMM-toolbox
A Library of ADMM for Sparse and Low-rank Optimization
Robust-PCA-with-ADMM
Performing Foreground Detection in videos using RPCA with ADMM algorithm
cuda-rpca-admm
A CUDA implementation of performing Robust PCA for foreground-background separation, using ADMM for optimization.