Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Single depth image super-resolution via high-frequency subbands enhancement and bilateral filtering(Institute of Electrical and Electronics Engineers Inc., 2016) Balure, C.S.; Ramesh Kini, M.; Bhavsar, A.This paper addresses the problem of super-resolution (SR) from a single low-resolution (LR) depth image to a high-resolution (HR) depth image. A simple yet effective method has been proposed using Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), and by utilizing the gradient information of the interpolated LR image. We propose an intermediate stage to enhance the high-frequency subbands to recover the HR image for both noiseless and noisy scenarios. The proposed method has been validated on Middlebury dataset for different upsampling factors (i.e. 2, 4, 8) and is shown to be superior when compared with some related DWT and SWT based SR methods. We also demonstrate encouraging performance of the approach on noisy depth images. © 2016 IEEE.Item Depth image super-resolution: A review and wavelet perspective(Springer Verlag service@springer.de, 2017) Balure, C.S.; Ramesh Kini, M.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.