Alternating minimization for a single step TV-Stokes model for image
denoising
- URL: http://arxiv.org/abs/2009.11973v2
- Date: Tue, 29 Sep 2020 13:07:36 GMT
- Title: Alternating minimization for a single step TV-Stokes model for image
denoising
- Authors: Bin Wu, Xue-Cheng Tai, and Talal Rahman
- Abstract summary: The paper presents a fully coupled TV-Stokes model, and propose an algorithm based on alternating minimization of the objective functional.
A convergence analysis is given.
- Score: 4.471370467116141
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The paper presents a fully coupled TV-Stokes model, and propose an algorithm
based on alternating minimization of the objective functional whose first
iteration is exactly the modified TV-Stokes model proposed earlier. The model
is a generalization of the second order Total Generalized Variation model. A
convergence analysis is given.
Related papers
- Identification of Non-causal Graphical Models [0.0]
The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables.
We show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model.
arXiv Detail & Related papers (2024-10-12T10:40:46Z) - Scaling and renormalization in high-dimensional regression [72.59731158970894]
This paper presents a succinct derivation of the training and generalization performance of a variety of high-dimensional ridge regression models.
We provide an introduction and review of recent results on these topics, aimed at readers with backgrounds in physics and deep learning.
arXiv Detail & Related papers (2024-05-01T15:59:00Z) - A global optimization SAR image segmentation model can be easily
transformed to a general ROF denoising model [7.828096299183532]
We propose a novel locally statistical active contour model (LACM) based on Aubert-Aujol (AA) denoising model and variational level set method.
We transform the proposed model into a global optimization model by using convex relaxation technique.
Experiments using some challenging synthetic images and Envisat SAR images demonstrate the superiority of our proposed models.
arXiv Detail & Related papers (2023-12-08T23:26:57Z) - Solving Linear Inverse Problems Provably via Posterior Sampling with
Latent Diffusion Models [98.95988351420334]
We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models.
We theoretically analyze our algorithm showing provable sample recovery in a linear model setting.
arXiv Detail & Related papers (2023-07-02T17:21:30Z) - Weighted structure tensor total variation for image denoising [0.5120567378386615]
For image denoising problems, the structure tensor total variation (STV)-based models show good performances when compared with other competing regularization approaches.
We employ the anisotropic weighted matrix introduced in the anisotropic total variation (ATV) model to improve the STV model.
Our proposed weighted STV model can effectively capture local information from images and maintain their details during the denoising process.
arXiv Detail & Related papers (2023-06-18T05:37:38Z) - Super-model ecosystem: A domain-adaptation perspective [101.76769818069072]
This paper attempts to establish the theoretical foundation for the emerging super-model paradigm via domain adaptation.
Super-model paradigms help reduce computational and data cost and carbon emission, which is critical to AI industry.
arXiv Detail & Related papers (2022-08-30T09:09:43Z) - Model soups: averaging weights of multiple fine-tuned models improves
accuracy without increasing inference time [69.7693300927423]
We show that averaging the weights of multiple models fine-tuned with different hyper parameter configurations improves accuracy and robustness.
We show that the model soup approach extends to multiple image classification and natural language processing tasks.
arXiv Detail & Related papers (2022-03-10T17:03:49Z) - Speech Summarization using Restricted Self-Attention [79.89680891246827]
We introduce a single model optimized end-to-end for speech summarization.
We demonstrate that the proposed model learns to directly summarize speech for the How-2 corpus of instructional videos.
arXiv Detail & Related papers (2021-10-12T18:21:23Z) - Iterative regularization algorithms for image denoising with the
TV-Stokes model [4.09305676000817]
We propose a set of iterative regularization algorithms for the TV-Stokes model to restore images from noisy images with Gaussian noise.
We have experimental results that show improvement over the original method in the quality of the restored image.
arXiv Detail & Related papers (2020-09-24T22:55:18Z) - Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
Blind image restoration is a common yet challenging problem in computer vision.
We propose a novel blind image restoration method, aiming to integrate both the advantages of them.
Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
arXiv Detail & Related papers (2020-08-25T03:30:53Z) - A Weighted Difference of Anisotropic and Isotropic Total Variation for
Relaxed Mumford-Shah Color and Multiphase Image Segmentation [2.6381163133447836]
We present a class of piecewise-constant image segmentation models that incorporate a difference of anisotropic and isotropic total variation.
In addition, a generalization to color image segmentation is discussed.
arXiv Detail & Related papers (2020-05-09T09:35:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.