A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation

0563455 - ÚTIA 2023 RIV US eng J - Journal Article
Singh, S. - Singh, H. - Mittal, N. - Singh, H. - Hussien, A.G. - Šroubek, Filip
A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation.
Expert Systems With Applications. Roč. 209, č. 1 (2022), č. článku 118272. ISSN 0957-4174. E-ISSN 1873-6793
Institutional support: RVO:67985556
Keywords : infrared (IR) * visible image * image fusion * AOA * image segmentation * WLS
OECD category: Robotics and automatic control
Impact factor: 8.5, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2022/ZOI/sroubek-0563455.pdf https://www.sciencedirect.com/science/article/pii/S0957417422014129?via%3Dihub

Images are fused to produce a composite image by combining key characteristics of the source images in image fusion. It makes the fused image better for human vision and machine vision. A novel procedure of Infrared (IR) and Visible (Vis) image fusion is proposed in this manuscript. The main challenges of feature level image fusion are that it will introduce artifacts and noise in the fused image. To preserve the meaningful information without adding artifacts from the source input images, weight map computed from Arithmetic optimization algorithm (AOA) is used for the image fusion process. In this manuscript, feature level fusion is performed after refining the weight maps using a weighted least square optimization (WLS) technique. Through this, the derived salient object details are merged into the visual image without introducing distortion. To affirm the validity of the proposed methodology simulation results are carried for twenty-one image data sets. It is concluded from the qualitative and quantitative experimental analysis that the proposed method works well for most of the image data sets and shows better performance than certain traditional existing models.
Permanent Link: https://hdl.handle.net/11104/0336403