Faculty Publications
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Publications by NITK Faculty
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Item Depth image super-resolution with local medians and bilateral filtering(Institute of Electrical and Electronics Engineers Inc., 2016) Balure, C.S.; Ramesh Kini, M.; Bhavsar, A.In this paper, we propose an approach for depth image super-resolution (SR). Given a noisy low resolution (LR) depth image and its corresponding registered high resolution (HR) colour image, our approach improves the resolution of the LR image while suppressing noise. We use the segmentation of HR colour images as a cue for depth image super-resolution. Our method begins with a highly over-segmented color image (using well-known segmentation approaches such as mean shift (MS) or simple linear iterative clustering (SLIC), and an interpolated LR depth image. We then use a combination of the local medians in the depth image (corresponding to the colour segments) and bicubic interpolation, followed by bilateral filtering to compute the SR depth image. We performed experiments for higher magnification factors 4, 8 using the Middlebury depth image dataset and evaluate the SR performance using the PSNR and SSIM metrics. The experimental results show that proposed method (including some variants), while being relatively simplistic, shows an average improvement of 1.2dB and 1.7dB on noiseless and noisy data respectively, over the popular method of guided image filtering (GIF) for upsampling factor 8. © 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.Item Guidance-based improved depth upsampling with better initial estimate(Inderscience Publishers, 2021) Balure, C.S.; Ramesh Kini, M.Like optical images, depth images are also gaining popularity because of its use in many applications like robot navigation, augmented reality, 3DTV and more. The commercially available depth cameras generate depth images which suffer from low spatial resolution, corrupted with noise, and missing regions. Such images need to be super-resolved, denoised and inpainted before using them to have better accuracy. Super-resolution (SR) techniques can be used to produce a high-resolution output. Since SR is an ill-posed inverse problem, a good initial estimate is always a good regulariser to find the optimal solution. We propose an initial estimate as part of our SR pipeline, esp. ×8, which will helps in quick convergence and accurate output. We propose a cascade approach by combining residual interpolation (RI) method with anisotropic total generalised variation (ATGV) method, both uses HR guidance image. The improvements are shown qualitative and quantitative with different levels of noise. © 2021 Inderscience Publishers. All rights reserved.