Depth map super-resolution with cascaded pyramid structure

Abstract

Due to the limitation of equipment, the resolution of depth map is low. Depth edges often become blurred when the low-resolution depth image is upsampled. In this paper, we present the pyramid dense residual network (PDRN) to efficiently reconstruct the high-resolution images. The network takes residual network as the main frame and adopts the cascaded pyramid structure for phased upsampling. At each pyramid level, the modified dense block is used to acquire high frequency residual, especially the edge features and the skip connection branch in the residual structure is used to deal with the low frequency information. The network directly uses the low-resolution depth image as the initial input of the network and the subpixel convolution layers is used for upsampling. It reduces the computational complexity. The experiments indicate that the proposed method effectively solves the problem of blurred edge and obtains great results both in qualitative and quantitative.