级联金字塔结构的深度图超分辨率重建

由于成像设备的限制，深度图往往分辨率较低。对低分辨率深度图进行上采样时，通常会造成深度图的边缘模糊。当上采样因子较大时，这种问题尤为明显。本文提出金字塔密集残差网络，实现深度图超分辨率重建。整个网络以残差网络为主框架，采用级联的金字塔结构对深度图分阶段上采样。在每一阶段，采用简化的密集连接块获取图像的高频残差信息，尤其是底层的边缘信息，同时残差结构中的跳跃连接分支获取图像的低频信息。网络直接以原始低分辨率深度图作为输入，以亚像素卷积层进行上采样操作，减少了运算复杂度。实验结果表明，该方法有效地解决了图像深度边缘的模糊问题，在定性和定量评价上优于现有方法。

Depth map super-resolution with cascaded pyramid structure

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.