A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation. (15th December 2022)

• Record Type: Journal Article
• Authors: Singh, Simrandeep; Singh, Harbinder; Mittal, Nitin; Singh, Harbinder; Hussien, Abdelazim G.; Sroubek, Filip
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Infrared (IR) -- visible (Vis) image -- Image Fusion -- AOA -- Image segmentation -- WLS; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118272
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: It is proposed an efficient method for image Fusion. Weight maps are generated from segmentation using Arithmetic optimization algorithm. The weight maps are optimized using a weighted least square (WLS) technique. The quality of the fused results is verified using the ( Q AB / F ), SCD, SSIM, and N A B / F . The quality of the fused results is better than other competitor algorithms. Abstract: 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.