The Effect of Fusion Rule in Sparse-Based Infrared-Visible Image Fusion with Blotless-Update Dictionary Learning Algorithm

Authors

  • Lalit Kumar Saini, Pratistha Mathur

DOI:

https://doi.org/10.17762/msea.v71i4.1697

Abstract

Image fusion is the combination of information from multiple images to a single image for various benefits. These benefits are in the form of reducing time, cost, and resources. from many image fusion methods, transfer domain methods had the extra benefit of solving complex problems easily. It is also quite popular due to the wide range of new development in the same domain from time to time. Among the wide range of image fusion applications and types, infrared-visible image fusion is one of the important image fusion techniques. It?s also so much popular due to its various application areas. Security, surveillance, and industrial safety are some of its application areas. Safe driving is one of its application areas by producing high-quality fused infrared-visible images for drivers. In this study, different experiments are done by applying modifications in the basic image fusion rule of the recently proposed sparse representation (SR) based image fusion technique for medical image fusion. The approach is based on the use of the BLOTLESS-update dictionary learning technique as a modification in the SR-based general image fusion framework. The experimental results also confirm that for different-different applications the same fusion rule could not be applied in the same way. So the study and experiments on fusion rule variation are quite interesting and inclined us to develop new fusion rules for infrared-visible image fusion applicable in day and night driving conditions.

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Published

2022-12-31

How to Cite

Lalit Kumar Saini, Pratistha Mathur. (2022). The Effect of Fusion Rule in Sparse-Based Infrared-Visible Image Fusion with Blotless-Update Dictionary Learning Algorithm. Mathematical Statistician and Engineering Applications, 71(4), 9239–9249. https://doi.org/10.17762/msea.v71i4.1697

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Section

Articles