Depth Image Super Resolution Based on Edge-Guided Method

Dongsheng Zhou,Qiang Zhang,Jian Lu,Ruyi Wang

doi:10.3390/app8020298

Dongsheng Zhou, Qiang Zhang + Show 2 more

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https://doi.org/10.3390/app8020298

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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.

Highlights

Introduction and Related WorksIn recent years, with the rapid development of computer vision technology, the depth information of scenes becomes increasingly essential for many applications, such as 3D Reconstruction [1,2], Augmented Reality [3], Robot Navigation [4] and so on
The algorithm we propose operates on patches extracted from yl, aiming to estimate the corresponding patch from yh
Conventional SR methods can cause edges to be blurred and jagged. Aiming at solving this problem, this paper proposes an edge-guided SR method

Summary

Introduction

Introduction and Related WorksIn recent years, with the rapid development of computer vision technology, the depth information of scenes becomes increasingly essential for many applications, such as 3D Reconstruction [1,2], Augmented Reality [3], Robot Navigation [4] and so on. Some active sensors [5], such as Kinect and PMD (Photonic Mixer Device), can acquire depth information of scenes. This information will be used to create a depth image. Due to the theoretical and practical limitations, the achievable resolution of any depth imaging device is usually too low to meet the needs of many practical applications. How to improve depth image resolution is an urgent problem that needs to be solved. One way to solve this problem is to apply some sophisticated vision sensors.

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