Multimed Tools Appl https://doi.org/10.1007/s11042-018-6174-3 Performance enhanced hyperspectral and multispectral image fusion technique using ripplet type-II transform and deep neural networks for multimedia applications 1 2 K. Hariharan & N. R. Raajan Received: 29 March 2018 /Revised: 27 April 2018 /Accepted: 21 May 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Multispectral and hyper spectral image fusion aspires to improve the spectral information and spatial details. Previous fusion algorithms have concentrated on spectral information and spatial details, but those fused images have missed its sharpening. This paper is introduced the ripple type-II (RT-II) transform and deep neural network (DNN). RT -II transform can be decomposed both multispectral and hyper spectral images, then DNN are used for recognize the complementary features and sharpened the decomposed images. Then applied the fused rules for fuse the both images and applied inverse RT-II transform to get fused image. In this paper, the proposed method gets better entropy, standard deviation (SD), Correlation Coefficient (CC), Edge-Dependent Fusion Quality Index (EDFQI), Edge Based Similarity Measure (EBSM), Structural similarity (SSIM) as compared with other methods. The best way of analyzing the concepts of date and image fusion methods is to perform fusion based analysis in
Multimedia Tools and Applications – Springer Journals
Published: Jun 5, 2018
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