English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Curvature Filters Efficiently Reduce Certain Variational Energies.

Gong, Y., & Sbalzarini, I. F. (2017). Curvature Filters Efficiently Reduce Certain Variational Energies. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 26(4), 1786-1798. doi:10.1109/TIP.2017.2658954.

Item is

Files

show Files

Locators

show
hide
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Gong, Yuanhao1, Author           
Sbalzarini, Ivo F.1, Author           
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, ou_2340692              

Content

show
hide
Free keywords: -
 Abstract: In image processing, the rapid approximate solution of variational problems involving generic data-fitting terms is often of practical relevance, for example in real-time applications. Variational solvers based on diffusion schemes or the Euler-Lagrange equations are too slow and restricted in the types of data-fitting terms. Here, we present a filter-based approach to reduce variational energies that contain generic data-fitting terms, but are restricted to specific regularizations. Our approach is based on reducing the regularization part of the variational energy, while guaranteeing non-increasing total energy. This is applicable to regularization-dominated models, where the data-fitting energy initially increases, while the regularization energy initially decreases. We present fast discrete filters for regularizers based on Gaussian curvature, mean curvature, and total variation. These pixel-local filters can be used to rapidly reduce the energy of the full model. We prove the convergence of the resulting iterative scheme in a greedy sense, and we show several experiments to demonstrate applications in image-processing problems involving regularization-dominated variational models.

Details

show
hide
Language(s):
 Dates: 2017-04-01
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/TIP.2017.2658954
Other: cbg-6799
PMID: 28141519
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
  Other : IEEE Trans Image Process
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 26 (4) Sequence Number: - Start / End Page: 1786 - 1798 Identifier: -