Three-dimensional object visualization and detection in low light illumination using integral imaging: an overview

We overview a recently published work that utilizes three-dimensional (3D) integral imaging (InIm) to capture 3D information of a scene in low illumination conditions using passive imaging sensors. An object behind occlusion is imaged using 3D InIm. A computational 3D reconstructed image is generated from the captured scene information at a particular depth plane, which showed the object without occlusion. Moreover, 3D InIm substantially increases the signal-to-noise ratio of the 3D reconstructed scene compared with a single two-dimensional (2D) image as readout noise is minimized. This occurs due to the 3D InIm reconstruction algorithm being naturally optimum in the maximumlikelihood sense in the presence of additive Gaussian noise. After 3D InIm reconstruction, facial detection using the Viola-Jones object detection framework is successful whereas it fails using a single two-dimensional (2D) elemental image.