Compressive Point Cloud Super Resolution

• Main Author: Smith, Cody S.
• Format: Others
• Published: DigitalCommons@USU 2012
• Subjects: Compressive Imaging; Compressive Sampling; Ladar; Super Resolution; Electrical and Computer Engineering
• Online Access: https://digitalcommons.usu.edu/etd/1392; https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2415&context=etd

Automatic target recognition (ATR) is the ability for a computer to discriminate between different objects in a scene. ATR is often performed on point cloud data from a sensor known as a Ladar. Increasing the resolution of this point cloud in order to get a more clear view of the object in a scene would be of significant interest in an ATR application. A technique to increase the resolution of a scene is known as super resolution. This technique requires many low resolution images that can be combined together. In recent years, however, it has become possible to perform super resolution on a single image. This thesis sought to apply Gabor Wavelets and Compressive Sensing to single image super resolution of digital images of natural scenes. The technique applied to images was then extended to allow the super resolution of a point cloud.