Deep Depth Graph Super Resolution Sampling Based on Depth Learning

Abstract

In the scene of depth image acquisition,in order to use the high-resolution color map of the scene for superresolution upper sampling,this paper proposes an adaptive learning algorithm for local filter kernels using convolutional neural network.It utilizes both the dense/high-resolution color and the sparse/low-resolution depth information to extract the scene informationentirely.Experimental results on the Middlebury and ToFMark datasets show that,compared with traditional depth superresolution algorithms,the proposed algorithm is capable of obtaining the best super-resolution results.Especially in the color and depth edge as well as the texture mismatch region,it has better robustness.