Paper Info
Title
Multifocus image fusion and denoising: A variational approach
Abstract
In this letter we propose a variational approach for concurrent image fusion and denoising of multifocus images, based on error estimation theory and Partial Differential Equations (PDEs). In real world scenarios the assumption that the inputs of an image fusion process contain only useful information, pertinent to the desired fused output, does not hold true more often than not. Thus, the image fusion problem needs to be addressed from a more complex, fusion-denoising point of view, in order to provide a fused result of greater quality. The novelty of our approach consists in defining an image geometry-driven, anisotropic fusion model, numerically expressed using an anisotropy-reinforcing discretization scheme that further increases the anisotropic behavior of the proposed fusion paradigm. The preliminary experimental analysis shows that robust anisotropic denoising can be attained in parallel with efficient image fusion, thus bringing two paramount image processing tasks into complete synergy. One immediate application of the proposed method is fusion of multifocus, noise-corrupted images.
Year
DOI
Venue
2012
10.1016/j.patrec.2012.02.017
Pattern Recognition Letters
Keywords
Field
DocType
variational approach,multifocus image fusion,proposed fusion paradigm,concurrent image fusion,image geometry-driven,efficient image fusion,paramount image processing task,anisotropic fusion model,multifocus image,image fusion process,image fusion problem,noise-corrupted image,partial differential equations,denoising,image restoration
Noise reduction,Computer vision,Discretization,Image fusion,Fusion,Image processing,Artificial intelligence,Estimation theory,Image restoration,Partial differential equation,Mathematics
Journal
Volume
Issue
ISSN
33
10
0167-8655
Citations 
PageRank 
References 
9
0.54
18
Authors
2
Name
Order
Citations
PageRank
Cosmin Ludusan1111.30
Olivier Lavialle2729.51