Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Creat membership Creat membership
Sign in

Forgot password?

Confirm
  • Forgot password?
    Sign Up
  • Confirm
    Sign In
Collection

toTop

If you have any feedback, Please follow the official account to submit feedback.

Turn on your phone and scan

home > search >

Convex Image Denoising via Non-convex Regularization with Parameter Selection

Author:
Lanza, Alessandro  Morigi, Serena  Sgallari, Fiorella  


Journal:
JOURNAL OF MATHEMATICAL IMAGING AND VISION


Issue Date:
2016


Abstract(summary):

We introduce a convex non-convex (CNC) denoising variational model for restoring images corrupted by additive white Gaussian noise. We propose the use of parameterized non-convex regularizers to effectively induce sparsity of the gradient magnitudes in the solution, while maintaining strict convexity of the total cost functional. Some widely used non-convex regularization functions are evaluated and a new one is analyzed which allows for better restorations. An efficient minimization algorithm based on the alternating direction method of multipliers (ADMM) strategy is proposed for simultaneously restoring the image and automatically selecting the regularization parameter by exploiting the discrepancy principle. Theoretical convexity conditions for both the proposed CNC variational model and the optimization sub-problems arising in the ADMM-based procedure are provided which guarantee convergence to a unique global minimizer. Numerical examples are presented which indicate how the proposed approach is particularly effective and well suited for images characterized by moderately sparse gradients.


Page:
195---220


Similar Literature

Submit Feedback

This function is a member function, members do not limit the number of downloads