Multi-focus Image Fusion Based on Similarity Characteristics

TL;DR

This paper introduces a new multi-focus image fusion method in the spatial domain that uses a similarity measure based on SSIM, combined with region segmentation techniques, to improve fusion quality and visual perception.

Contribution

A novel multi-focus image fusion algorithm utilizing a new SSIM-based similarity measure and advanced segmentation methods for enhanced fusion performance.

Findings

Effective fusion with improved visual quality

High edge retention and detail preservation

Superior performance on evaluation metrics

Abstract

A novel multi-focus image fusion algorithm performed in spatial domain based on similarity characteristics is proposed incorporating with region segmentation. In this paper, a new similarity measure is developed based on the structural similarity (SSIM) index, which is more suitable for multi-focus image segmentation. Firstly, the SSNSIM map is calculated between two input images. Then we segment the SSNSIM map using watershed method, and merge the small homogeneous regions with fuzzy c-means clustering algorithm (FCM). For three source images, a joint region segmentation method based on segmentation of two images is used to obtain the final segmentation result. Finally, the corresponding segmented regions of the source images are fused according to their average gradient. The performance of the image fusion method is evaluated by several criteria including spatial frequency, average gradient, entropy, edge retention etc. The evaluation results indicate that the proposed method is effective and has good visual perception.