Resolution Enhancement in Single Depth mAP and Aligned Image

Abstract

Depth resolution enhancement aims to recover a high quality depth map from one or multiple low-resolution depth input(s) with missing pixels. While a registered high-resolution intensity image is often utilized to assist, little attention has been paid to the circumstances when there is only one pair of low-resolution depth map and aligned intensity image available. In this paper, we propose a novel resolution enhancement approach that targets at improving the quality of both the input depth map and the low-resolution RGB image. By exploiting the statistical dependency between the input pairs, a label matrix is generated utilizing the support vector machine classifier. Guided by the constructed label matrix and the aligned intensity image, the missing values in the depth map are well predicted in a manner consistent with the embedded structure. After that, the completed depth map and the intensity image are super-resolved through a set of regression models trained via external exemplars. Extensive experiments demonstrate that our framework is effective with satisfying performance.