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Anisotropic total generalized variation model for Poisson noise removal
When removing Poisson noise, it is a challenging task to overcome the staircase effect and maintain edge details. To achieve this goal, this paper...
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Low-rank matrix recovery with total generalized variation for defending adversarial examples
Low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure....
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Revisiting reweighted graph total variation blind deconvolution and beyond
It is known that image priors are essential to blind deconvolution. Reweighted graph total variation (RGTV), as a new prior to substitute the most...
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The Total Variation-Wasserstein Problem: A New Derivation of the Euler-Lagrange Equations
In this work we analyze the Total Variation-Wasserstein minimization problem. We propose an alternative form of deriving optimality conditions from...
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Deep Image Prior Regularized by Coupled Total Variation for Image Colorization
Automatic image colorization is an old problem in image processing that has regained interest in the recent years with the emergence of deep-learning...
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Image denoising based on the fractional-order total variation and the minimax-concave
The total variation model has attracted considerable attention for its good balance of noise reduction and edge maintenance, but it produces blocky...
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Total variation image reconstruction algorithm based on non-convex function
The total variation method has been widely used because it can preserve important sharp edges and target boundaries in the image, but one of its...
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Robust principal component analysis via weighted nuclear norm with modified second-order total variation regularization
The traditional robust principal component analysis (RPCA) model aims to decompose the original matrix into low-rank and sparse components and uses...
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A bilevel optimization problem with deep learning based on fractional total variation for image denoising
In this work, we introduce a bilevel problem based on fractional-order total variation for image denoising. A deep learning architecture is provided...
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Infimal convolution and AM-GM majorized total variation-based integrated approach for biosignal denoising
Biomedical measurements are generally contaminated with substantial noise from various sources, including thermal noise, interference from other...
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Structure–texture image decomposition via non-convex total generalized variation and convolutional sparse coding
Image decomposition is a fundamental but challenging ill-posed problem in image processing and has been widely applied to compression, enhancement,...
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Weighted hybrid order total variation model using structure tensor for image denoising
A total variation filter has the characteristic of edge protection and has been widely used in image denoising for many years. In this study, our aim...
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Compressive sensing spatially adaptive total variation method for high-noise astronomical image denoising
High-noise astronomical-image denoising has always been a research hotspot in deep space exploration. Compressive sensing (CS) is an advanced...
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Total variable-order variation as a regularizer applied on multi-frame image super-resolution
Multi-frame image super-resolution reconstruction focuses on obtaining a high-resolution (HR) image from a low-resolution image. Since the...
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Proximal alternating minimization method for Poisson noise removal
In this paper, utilizing the quadratic penalty technique and adding a proximal term in one subproblem, we propose the proximal alternating...
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Towards Off-the-Grid Algorithms for Total Variation Regularized Inverse Problems
We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most...
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Property Constrained Video Summarization via Regret Minimization
Video summarization has become one of the most effective solutions for quickly understanding a large amount of video data. Video properties such as...
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Image restoration with impulse noise based on fractional-order total variation and framelet transform
Restoring images corrupted by noise and blur is a burgeoning subject in image processing, and despite the large number of proposed restoration...
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The ℓ2,p regularized total variation with overlap** group sparsity prior for image restoration with impulse noise
In this paper, we consider ℓ 2, p (0 < p < 1) regularized total variation with overlap** group sparsity prior for image restoration with impulse...
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