Development of Multi Modal Image Fusion Techniques in Night Vision Technology Using Deep Learning

Abstract

This study is aimed at exploring new development in nighttime image processing based on nighttime photography/night vision. We use various learning methodologies based on neural networks to develop multisensor data processing techniques. The ability of Convolutional Neural Networks (CNN) to enhance image quality and clarity under conditions of low light or at night is the focus of our research. To do this A large database has been created, containing 3200 images both taken from the internet as well as our own. This data is preprocessed, normalized and features expanded using some common methods of data augmentation in order to optimize model performance. We compared our proposed CNN model with existing literature through a series of experiments. Performance was measured using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The Results are now in. They showed that our model can improve various metrics with higher scores. And they show the real-time monitoring capabilities in urban streets for our model. This research brings multisensor image fusion based on night-adjacent techniques forward from night vision technology and provides insight into practical and potential future directions for using deep learning techniques to improve safety and security in many different environments.