Image fusion via non-local sparse K-SVD dictionary learning

Ying Li, Fangyi Li, Bendu Bai, Qiang Shen

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

30 Dyfyniadau (Scopus)
266 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.
Iaith wreiddiolSaesneg
Tudalennau (o-i)1814-1823
CyfnodolynApplied Optics
Cyfrol55
Rhif cyhoeddi7
Dyddiad ar-lein cynnar22 Ion 2016
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Maw 2016

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