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Abstract-Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral imaging based Multidimensional Optical Sensing and Imaging Systems (MOSIS), which can be used for 3D visualization, seeing through obscurations, material inspection, and object recognition from micro scales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2D) images or conventional 3D images. In addition, we present recent progress and applications of 3D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave infrared photon counting, 3D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid crystal devices, and 3D endoscopy for healthcare applications.

Abstract

An approach that uses the scene information acquired by means of a 3D Synthetic Aperture Integral Imaging system is presented. This method generates a depth map of the scene through a voting strategy. In particular, we consider the information given by each camera of the array for each pixel, and also the local information in a neighbourhood of that pixel. The proposed method obtains consistent results for any type of object surfaces as well as very sharp boundaries.In addition, we also contribute in this paper with a repository of a set of synthetic integral images generated by 3DS Max where the so called ground truth (real-true depth map) is available. This resource can be used as a benchmark to test any Integral Imaging based range estimation method.