Registration of multi-modal images under a complex background combining multiscale features extraction and semantic segmentation.

Abstract

Multi-modal imaging technology has a very broad application value in target recognition and other fields, and image registration is one of its key technologies. In this paper, a multi-modal image registration algorithm that combines multiscale features extraction and semantic segmentation is proposed to achieve accurate registration of polarized images and near-infrared images under complex backgrounds. A classical convolutional neural network ResNet is employed to capture the robust feature descriptors, and a convolutional neural network with an attention mechanism is trained to filter out the irrelevant feature points. Further, the two multi-modal images can be further registered. The experimental results show the feasibility and effectiveness of the proposed method.