Image fusion via nonlocal sparse K-SVD dictionary learning

Ying Li, Fangyi Li, Bendu Bai, Qiang Shen
2016 Applied Optics  
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
more » ... , an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The non-local 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 non-local self-similarity is combined with traditional sparse dictionary [1], resulting in an improved learned dictionary, that is hereafter referred to as the non-local sparse K-SVD (NL_SK_SVD) dictionary. 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 image, 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.
doi:10.1364/ao.55.001814 pmid:26974648 fatcat:elkd66egtbh35jwj2cbxnmysem