Single Depth Image Super-Resolution Using Convolutional Neural Networks

Abstract

In this paper, we propose single depth image super-resolution using convolutional neural networks (CNN). We adopt CN-N to acquire a high-quality edge map from the input low-resolution (LR) depth image. We use the high-quality edge map as the weight of the regularization term in a total variation (TV) model for super-resolution. First, we interpolate the LR depth image using bicubic interpolation and extract its low-quality edge map. Then, we get the high-quality edge map from the low-quality one using CNN. Since the CNN output often contains broken edges and holes, we refine it using the low-quality edge map. Guided by the high-quality edge map, we upsample the input LR depth image in the TV model. The edge-based guidance in TV effectively removes noise in depth while minimizing jagged artifacts and preserving sharp edges. Various experiments on the Middle-bury stereo dataset and Laser Scan dataset demonstrate the superiority of the proposed method over state-of-the-arts in both qualitative and quantitative measurements.