Depth image super-resolution: A review and wavelet perspective

Abstract

We propose an algorithm which utilizes the Discrete Wavelet Transform (DWT) to super-resolve the low-resolution (LR) depth image to a high-resolution (HR) depth image. Commercially available depth cameras capture depth images at a very low-resolution as compared to that of the optical cameras. Having an highresolution depth camera is expensive because of the manufacturing cost of the depth sensor element. In many applications like robot navigation, human-machine interaction (HMI), surveillance, 3D viewing, etc. where depth images are used, the LR images from the depth cameras will restrict these applications, thus there is a need of a method to produce HR depth images from the available LR depth images. This paper addresses this issue using DWT method. This paper also contributes to the compilation of the existing methods for depth image super-resolution with their advantages and disadvantages, along with a proposed method to super-resolve depth image using DWT. Haar basis for DWT has been used as it has an intrinsic relationship with super-resolution (SR) for retaining the edges. The proposed method has been tested on Middlebury and Tsukuba dataset and compared with the conventional interpolation methods using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance metrics. � Springer Science+Business Media Singapore 2017.