Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation
Since a noisy image has inferior characteristics, the direct use of Fuzzy-Means (FCM) to
segment it often produces poor image segmentation results. Intuitively, using its ideal value …
segment it often produces poor image segmentation results. Intuitively, using its ideal value …
TV: A Sparse Optimization Method for Impulse Noise Image Restoration
G Yuan, B Ghanem - IEEE transactions on pattern analysis and …, 2017 - ieeexplore.ieee.org
Total Variation (TV) is an effective and popular prior model in the field of regularization-
based image processing. This paper focuses on total variation for removing impulse noise in …
based image processing. This paper focuses on total variation for removing impulse noise in …
Jump-sparse and sparse recovery using Potts functionals
M Storath, A Weinmann… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
We recover jump-sparse and sparse signals from blurred incomplete data corrupted by
(possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical …
(possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical …
l0tv: A new method for image restoration in the presence of impulse noise
G Yuan, B Ghanem - … of the IEEE conference on Computer …, 2015 - openaccess.thecvf.com
Total Variation (TV) is an effective and popular prior model in the field of regularization-
image processing. This paper focuses on TV for image restoration in the presence of …
image processing. This paper focuses on TV for image restoration in the presence of …
Proximity algorithms for the L1/TV image denoising model
This paper introduces a proximity operator framework for studying the L1/TV image
denoising model which minimizes the sum of a data fidelity term measured in the ℓ 1-norm …
denoising model which minimizes the sum of a data fidelity term measured in the ℓ 1-norm …
A fractional-order adaptive regularization primal–dual algorithm for image denoising
D Tian, D Xue, D Wang - Information Sciences, 2015 - Elsevier
This paper aims to develop a fractional-order model and a primal–dual algorithm for image
denoising, where a regularization parameter can be adjusted adaptively according to …
denoising, where a regularization parameter can be adjusted adaptively according to …
A TV-log nonconvex approach for image deblurring with impulsive noise
In this paper, we study the image deblurring with impulsive noise problem. In order to find a
high quality recovery solution, we propose a nonconvex optimization model that combines …
high quality recovery solution, we propose a nonconvex optimization model that combines …
Total variation with overlapping group sparsity for image deblurring under impulse noise
G Liu, TZ Huang, J Liu, XG Lv - PloS one, 2015 - journals.plos.org
The total variation (TV) regularization method is an effective method for image deblurring in
preserving edges. However, the TV based solutions usually have some staircase effects. In …
preserving edges. However, the TV based solutions usually have some staircase effects. In …
A semismooth Newton method for nonlinear parameter identification problems with impulsive noise
This work is concerned with nonlinear parameter identification in partial differential
equations subject to impulsive noise. To cope with the non-Gaussian nature of the noise, we …
equations subject to impulsive noise. To cope with the non-Gaussian nature of the noise, we …
Multi-step fixed-point proximity algorithms for solving a class of optimization problems arising from image processing
We introduce in this paper a class of multi-step fixed-point proximity algorithms for solving
optimization problems in the context of image processing. The objective functions of such …
optimization problems in the context of image processing. The objective functions of such …