Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

Abstract

The current trend in Image Fusion (IF) algorithms concentrate on the fusion process alone. However, pay less attention to critical issues such as the similarity between the two input images, features that participate in the Image Fusion. This paper addresses these two issues by deliberately attempting a new Image Fusion framework with Convolutional Neural Network (CNN). CNN has features like pre-training and similarity score, but functionalities are limited. A CNN model with classification prediction and similarity estimation are introduced as Classification Similarity Networks (CSN) to address these issues. ResNet50 and GoogLeNet are modified as the classification branches of CSN v1, CSN v2, respectively, to reduce feature dimensions. IF rules depend on the input dataset to fusion the extracted features. The output of the fusion process is fed into CSN v3 to improve the output image quality. The proposed CSN model is pre-trained and Fully Convolutional. At the time of IF, consider the similarities between the input images. This model applies to Multi-Focus, Multi-Modal Medical, Infrared-Visual and Multi-Exposure image datasets, and analyzed outcomes. The suggested model shows a significant improvement than the modern IF algorithms.