A nonlinear total variation-based denoising method with two regularization parameters
Related Experiment Videos
1Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA 16802, USA. csd12@psu.edu
Abstract:
The aim of the present paper is to study the effect of the regularization parameter used in the numerical implementation of the Rudin-Osher-Fatemi denoising model. By using two different regularization parameters in the numerical scheme of the Rudin-Osher-Fatemi model, we will show experimentally that when a particular relationship between the sizes of these parameters holds, the quality of the denoised image and the speed of convergence of the numerical scheme are both much improved in comparison with the classic numerical scheme of the Rudin-Osher-Fatemi model where only one regularization parameter is used.
Related Concept Videos
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Linearization and Approximation
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Application of Linearization and Approximation
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...