A Non-Local Low-Rank Approach to Enforce Integrability - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2016

A Non-Local Low-Rank Approach to Enforce Integrability

Hicham Badri
  • Function : Author
  • PersonId : 772557
  • IdRef : 19070506X
Hussein Yahia

Abstract

We propose a new approach to enforce integrability using recent advances in non-local methods. Our formulation consists in a sparse gradient data-fitting term to handle outliers together with a gradient-domain non-local low-rank prior. This regularization has two main advantages : 1) the low-rank prior ensures similarity between non-local gradient patches, which helps recovering high-quality clean patches from severe outliers corruption, 2) the low-rank prior efficiently reduces dense noise as it has been shown in recent image restoration works. We propose an efficient solver for the resulting optimization formulation using alternate minimization. Experiments show that the new method leads to an important improvement compared to previous optimization methods and is able to efficiently handle both outliers and dense noise mixed together.
Fichier principal
Vignette du fichier
lowrank_ieee_tip.pdf (2.82 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01317151 , version 1 (18-05-2016)

Identifiers

  • HAL Id : hal-01317151 , version 1

Cite

Hicham Badri, Hussein Yahia. A Non-Local Low-Rank Approach to Enforce Integrability. IEEE Transactions on Image Processing, 2016. ⟨hal-01317151⟩

Collections

INRIA INRIA2
178 View
302 Download

Share

Gmail Facebook X LinkedIn More