Results 61 to 70 of about 19,283,190 (228)

IRSnet: An Implicit Residual Solver and Its Unfolding Neural Network With 0.003M Parameters for Total Variation Models

IEEE Access
Solving total variation problems is fundamentally important for many computer vision tasks, such as image smoothing, optical flow estimation and 3D surface reconstruction. However, the traditional iterative solvers require a large number of iterations to
Yuanhao Gong
doaj   +1 more source

Total Generalized Variation for Manifold-valued Data

, 2018
In this paper we introduce the notion of second-order total generalized variation (TGV) regularization for manifold-valued data in a discrete setting. We provide an axiomatic approach to formalize reasonable generalizations of TGV to the manifold setting
Bredies, K., Holler, M., Storath, M., Weinmann, A.   +3 more
core   +1 more source

Deep Unfolding Network for Multi-Band Images Synchronous Fusion

IEEE Access, 2023
This study proposes a new deep neural network to solve the multi-band image synchronous fusion problem (MBF-Net). Unlike other deep learning-based methods, our network architecture design combines the ideas of model-driven and data-driven methods, so it ...
Dong Yu, Suzhen Lin, Xiaofei Lu, Dawei Li, Yanbo Wang   +4 more
doaj   +1 more source

On-the-fly Approximation of Multivariate Total Variation Minimization [PDF]

, 2016
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data.
Abry, Patrice, Condat, Laurent, Frecon, Jordan, Pustelnik, Nelly   +3 more
core   +2 more sources

Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging.
Yin Zhang, Qiping Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang   +4 more
doaj   +1 more source

Asymptotic behaviour of total generalised variation [PDF]

, 2015
The recently introduced second order total generalised variation functional $\mathrm{TGV}_{\beta,\alpha}^{2}$ has been a successful regulariser for image processing purposes.
Papafitsoros, Konstantinos, Valkonen, Tuomo   +1 more
core  

Total variation regularization for manifold-valued data

, 2013
We consider total variation minimization for manifold valued data. We propose a cyclic proximal point algorithm and a parallel proximal point algorithm to minimize TV functionals with $\ell^p$-type data terms in the manifold case.
Demaret, Laurent, Storath, Martin, Weinmann, Andreas   +2 more
core   +1 more source

Hyperspectral Image Mixed Noise Removal via Double Factor Total Variation Nonlocal Low-Rank Tensor Regularization

Remote Sensing
A hyperspectral image (HSI) is often corrupted by various types of noise during image acquisition, e.g., Gaussian noise, impulse noise, stripes, deadlines, and more. Thus, as a preprocessing step, HSI denoising plays a vital role in many subsequent tasks.
Yongjie Wu, Wei Xu, Liangliang Zheng
doaj   +1 more source

Total variation error bounds for geometric approximation

, 2013
We develop a new formulation of Stein's method to obtain computable upper bounds on the total variation distance between the geometric distribution and a distribution of interest.
Peköz, Erol A., Ross, Nathan, Röllin, Adrian   +2 more
core   +2 more sources

Optimizing Dynamic Mode Decomposition for Video Denoising via Plug-and-Play Alternating Direction Method of Multipliers

Signals
Dynamic mode decomposition (DMD) is a powerful tool for separating the background and foreground in videos. This algorithm decomposes a video into dynamic modes, called DMD modes, to facilitate the extraction of the near-zero mode, which represents the ...
Hyoga Yamamoto, Shunki Anami, Ryo Matsuoka   +2 more
doaj   +1 more source