Camera Array Synthetic Aperture Focusing and Fusion Based Hidden Object Imaging

Abstract

Hidden object imaging is challenging problem in the fields of computer vision and image processing, and it’s a key step in many application fields, include intelligent video surveillance, visual tracking and scene understanding. Recently, the camera array synthetic aperture imaging has been proved to be a powerful technology for hidden object detection, and the state-of-art synthetic imaging method can focus on multiple parallel planes so as to achieve seeing hidden through severe occlusion. However, due to the depth variation of hidden object’s surface, it’s difficult for existing method to get a complete clear image. This paper presents a novel synthetic aperture imaging and fusion approach, the main characteristics of proposed method include three parts: (1) A novel imaging framework is presented which integrate synthetic aperture imaging and fusion seamlessly. (2) Several multiple scale image fusion methods have been adopted in our framework to create high resolution and highly detailed images of occluded object. (3)A camera array synthetic aperture imaging and fusion system has been developed based on the proposed approach, and the Stanford light field public dataset and our camera array occlusion object dataset have been used to evaluate the performance of this algorithm. Extensive experiment results demonstrate that compared to the existing state-of-art camera array synthetic aperture imaging approach, our approach can enhance the image detail and quality of hidden objects under occlusion.