HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Exact discrete minimization for TV+L0 image decomposition models

Abstract : Penalized maximum likelihood denoising approaches seek a solution that fulfills a compromise between data fidelity and agreement with a prior model. Penalization terms are generally chosen to enforce smoothness of the solution and to reject noise. The design of a proper penalization term is a difficult task as it has to capture image variability. Image decomposition into two components of different nature, each given a different penalty, is a way to enrich the modeling. We consider the decomposition of an image into a component with bounded variations and a sparse component. The corresponding penalization is the sum of the total variation of the first component and the L0 pseudo-norm of the second component. The minimization problem is highly non-convex, but can still be globally minimized by a minimum s-t-cut computation on a graph. The decomposition model is applied to synthetic aperture radar image denoising.
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download

https://hal-ujm.archives-ouvertes.fr/ujm-00985427
Contributor : Loïc Denis Connect in order to contact the contributor
Submitted on : Tuesday, April 29, 2014 - 4:25:30 PM
Last modification on : Tuesday, October 19, 2021 - 6:57:34 PM
Long-term archiving on: : Tuesday, July 29, 2014 - 1:20:57 PM

File

graphcuts_image_decomposition_...
Files produced by the author(s)

Identifiers

Citation

Loïc Denis, Florence Tupin, X. Rondeau. Exact discrete minimization for TV+L0 image decomposition models. Image Processing (ICIP), 2010 17th IEEE International Conference on, Sep 2010, Hong Kong SAR China. pp.2525 - 2528, ⟨10.1109/ICIP.2010.5649204⟩. ⟨ujm-00985427⟩

Share

Metrics

Record views

187

Files downloads

269