Search results for "Total variation denoising"
showing 8 items of 8 documents
Blind deconvolution using TV regularization and Bregman iteration
2005
In this paper we formulate a new time dependent model for blind deconvolution based on a constrained variational model that uses the sum of the total variation norms of the signal and the kernel as a regularizing functional. We incorporate mass conservation and the nonnegativity of the kernel and the signal as additional constraints. We apply the idea of Bregman iterative regularization, first used for image restoration by Osher and colleagues [S.J. Osher, M. Burger, D. Goldfarb, J.J. Xu, and W. Yin, An iterated regularization method for total variation based on image restoration, UCLA CAM Report, 04-13, (2004)]. to recover finer scales. We also present an analytical study of the model disc…
Total Variation Regularization in Digital Breast Tomosynthesis
2013
We developed an iterative algebraic algorithm for the reconstruction of 3D volumes from limited-angle breast projection images. Algebraic reconstruction is accelerated using the graphics processing unit. We varied a total variation (TV)-norm parameter in order to verify the influence of TV regularization on the representation of small structures in the reconstructions. The Barzilai-Borwein algorithm is used to solve the inverse reconstruction problem. The quality of our reconstructions was evaluated with the Quart Mam/Digi Phantom, which features so-called Landolt ring structures to verify perceptibility limits. The evaluation of the reconstructions was done with an automatic LR detection a…
MRI resolution enhancement using total variation regularization
2009
We propose a novel method for resolution enhancement for volumetric images based on a variational-based reconstruction approach. The reconstruction problem is posed using a deconvolution model that seeks to minimize the total variation norm of the image. Additionally, we propose a new edge-preserving operator that emphasizes and even enhances edges during the up-sampling and decimation of the image. The edge enhanced reconstruction is shown to yield significant improvement in resolution, especially preserving important edges containing anatomical information. This method is demonstrated as an enhancement tool for low-resolution, anisotropic, 3D brain MRI images, as well as a pre-processing …
Denoising of MR spectroscopy signals using total variation and iterative Gauss-Seidel gradient updates
2015
We present a fast variational approach for denoising signals from magnetic resonance spectroscopy (MRS). Differently from the TV approaches applied to denoising of images, this is the first time to our knowledge that it has been used for the processing of free induction decay signals from single-voxel spectroscopy (SVS) acquisitions. Another novelty in this study is the direct use of the Euler Lagrange formulation coupled with Gauss Seidel gradient updates to improve the speed of iteration and reduce ringing. Results from brain MRS signals show improvement in signal to noise ratio as well as reduction in estimation error in the quantification of metabolites.
A note on the Bregmanized Total Variation and dual forms
2009
This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches.
Subsignal-based denoising from piecewise linear or constant signal
2011
15 pages; International audience; n the present work, a novel signal denoising technique for piecewise constant or linear signals is presented termed as "signal split." The proposed method separates the sharp edges or transitions from the noise elements by splitting the signal into different parts. Unlike many noise removal techniques, the method works only in the nonorthogonal domain. The new method utilizes Stein unbiased risk estimate (SURE) to split the signal, Lipschitz exponents to identify noise elements, and a polynomial fitting approach for the sub signal reconstruction. At the final stage, merging of all parts yield in the fully denoised signal at a very low computational cost. St…
Total-variation-based methods for gravitational wave denoising
2014
We describe new methods for denoising and detection of gravitational waves embedded in additive Gaussian noise. The methods are based on Total Variation denoising algorithms. These algorithms, which do not need any a priori information about the signals, have been originally developed and fully tested in the context of image processing. To illustrate the capabilities of our methods we apply them to two different types of numerically-simulated gravitational wave signals, namely bursts produced from the core collapse of rotating stars and waveforms from binary black hole mergers. We explore the parameter space of the methods to find the set of values best suited for denoising gravitational wa…
Modified total variation regularization using fuzzy complement for image denoising
2015
In this paper, we propose a denoising algorithm based on the Total Variation (TV) model. Specifically, we associate to the regularization term of the Rodin-Osher-Fatimi (ROF) functional a small weight whenever denoising is performed in edge and texture regions, which means less regularization and more details preservation. On the other hand, a large weight is associated if the region being filtered is smooth which means noise will be well suppressed. The weight computation is inspired from the fuzzy edge complement. Experiments on well-known images and comparison with state of the art denoising algorithms demonstrate that the proposed method not only presents good denoising performance but …