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Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

N. A. Lawrance*, T. S. Shiny Angel

Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu, 603203, India

* Corresponding Author: N. A. Lawrance. Email:

Computer Systems Science and Engineering 2023, 46(3), 3439-3455.


The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.


Cite This Article

N. A. Lawrance and T. S. Shiny Angel, "Image fusion based on nsct and sparse representation for remote sensing data," Computer Systems Science and Engineering, vol. 46, no.3, pp. 3439–3455, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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