Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …
Total variation regularization algorithms for images corrupted with different noise models: a review
P Rodríguez - Journal of Electrical and Computer Engineering, 2013 - Wiley Online Library
Total Variation (TV) regularization has evolved from an image denoising method for images
corrupted with Gaussian noise into a more general technique for inverse problems such as …
corrupted with Gaussian noise into a more general technique for inverse problems such as …
Robust video denoising using low rank matrix completion
Most existing video denoising algorithms assume a single statistical model of image noise,
eg additive Gaussian white noise, which often is violated in practice. In this paper, we …
eg additive Gaussian white noise, which often is violated in practice. In this paper, we …
An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise
We extend the alternating minimization algorithm recently proposed in Y. Wang, J. Yang, W.
Yin, and Y. Zhang, SIAM J. Imag. Sci., 1 (2008), pp. 248–272; J. Yang, W. Yin, Y. Zhang, and …
Yin, and Y. Zhang, SIAM J. Imag. Sci., 1 (2008), pp. 248–272; J. Yang, W. Yin, Y. Zhang, and …
Mixed noise removal via Laplacian scale mixture modeling and nonlocal low-rank approximation
Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse
noise is a challenging problem due to its difficulties in an accurate modeling of the …
noise is a challenging problem due to its difficulties in an accurate modeling of the …
Mixed noise removal by weighted encoding with sparse nonlocal regularization
Mixed noise removal from natural images is a challenging task since the noise distribution
usually does not have a parametric model and has a heavy tail. One typical kind of mixed …
usually does not have a parametric model and has a heavy tail. One typical kind of mixed …
Restoration of images corrupted by mixed Gaussian-impulse noise via l1–l0 minimization
In this paper, we study the restoration of images corrupted by Gaussian plus impulse noise,
and propose a l1–l0 minimization approach where the l1 term is used for impulse denoising …
and propose a l1–l0 minimization approach where the l1 term is used for impulse denoising …
Restoration of images corrupted by impulse noise and mixed Gaussian impulse noise using blind inpainting
M Yan - SIAM Journal on Imaging Sciences, 2013 - SIAM
This article studies the problem of image restoration of observed images corrupted by
impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse …
impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse …
Fast two-phase image deblurring under impulse noise
In this paper, we propose a two-phase approach to restore images corrupted by blur and
impulse noise. In the first phase, we identify the outlier candidates—the pixels that are likely …
impulse noise. In the first phase, we identify the outlier candidates—the pixels that are likely …
A weighted dictionary learning model for denoising images corrupted by mixed noise
This paper proposes a general weighted l 2-l 0 norms energy minimization model to remove
mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse …
mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse …