Accepted paper in SIBGRAPI 2015

Title: Simultaneously Estimation of Super-Resolution Images and Depth Maps from Low Resolution Sensors

Authors: Daniel B. Mesquita, Erickson R. Nascimento, Mario F. M. Campos

Abstract: The emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is called for. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, this work proposes a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining resolution intensity images and depth maps in a optimization process, our methodology combines low creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images
and depth maps acquired with low resolution, commercially available sensors.