Depth map super-resolution via iterative joint-trilateral-upsampling

Abstract

In this paper, we propose a new approach to solve the depth map super-resolution (SR) and denoising problems simultaneously. Inspired by joint-bilateral-upsampling (JBU), we devised the joint-trilateral-upsampling (JTU), which takes edge of the initial depth map, texture of the corresponding high-resolution color image and the values of the surrounding depth pixels, into consideration during the process of SR. To preserve the sharp edge of the up-sampled depth map and remove the noise, we introduce an iterative implementation, where current up-sampled depth map is fed into the next iteration, to refine the filter coefficients of JTU. The iterative JTU presents a high performance at many aspects such as sharping edge, denoising and none texture copying, etc. To demonstrate the superiority of the proposed method, we carry out various experiments and show an across-the-board quality improvement by both of subjective and objective evaluations compared with previous state-of-art methods.