Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features

Abstract

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.