Depth Image Super Resolution Based on Edge-Guided Method

Abstract

Depth image super-resolution (SR) is a technique which can reconstruct a high-resolution (HR) depth image from a low-resolution (LR) depth image. Its purpose is to obtain HR details to meet the needs of various applications in computer vision. In general, conventional depth image SR methods often cause edges in the final HR image to be blurred or ragged. To solve this problem, an edge-guided method for depth image SR is presented in this paper. To get high-quality edge information, a pair of sparse dictionaries was applied to reconstruct edges of depth image. Then, with the guidance of these high-quality edges, a depth image was interpolated by using a modified joint bilateral filter. Edge-guided method can preserve the sharpness of edges and effectively avoid generating blurry and ragged edges when SR is performed. Experiments showed that the proposed method can get better results on both subjective and objective evaluation, and the reconstructed performance was superior to conventional depth image SR methods.