Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Comprehensive address generator for digital signal processing(2009) Ramesh Kini, M.R.; Sumam David, S.Computational efficiency of Signal Processing Algorithm implemented in hardware depends on efficiency of datapath, memory speed, and generation of addresses for data access. In case of signal processing applications, pattern of data access is complex in comparison with other applications. If implemented in a general purpose processor, address generation for signal processing applications will require execution of a series of instructions and use of datapath elements like adders, shifters etc. In general, considerable processor resources and time are utilized. It is desirable to execute one loop of a kernel per clock. This demands generation of typically three addresses per clock: two addresses for data sample/coefficient and one for storage of processed data. A set of dedicated, efficient Address Generator Units (AGU) will definitely enhance the performance. This paper focuses on design and implementation of Address Generators for complex addressing modes required by Multimedia Signal Processing algorithms. Among other addressing modes, a novel algorithm is developed for accessing data in a Bit-Reversed order for Fast Fourier Transforms (FFT), and Zig-zag order for Discrete Cosine Transforms (DCT). When mapped to hardware, this scales linearly in gate complexity with increase in the size and uses less components. ©2009 IEEE.Item A Survey-Super Resolution Techniques for Multiple, Single, and Stereo Images(Institute of Electrical and Electronics Engineers Inc., 2014) Balure, C.S.; Ramesh Kini, M.R.This paper reviews some of the methods of super resolution (SR) (with multiple images and single image as input) with a focus on super resolution for stereo images with their advantages and disadvantages. This paper has attempted to fill the void of non availability of the survey of SR techniques for stereo image. © 2014 IEEE.Item Local segment-based dense depth reconstruction from very sparsely sampled data(Institute of Electrical and Electronics Engineers Inc., 2017) Balure, C.S.; Bhavsar, A.; Ramesh Kini, M.R.In this paper, we propose two relatively simplistic and efficient methods for depth reconstruction from very sparsely sampled random depth data. Both the proposed approaches exploit the segmentation cue from a registered colour image of the same scene. The first approach which we term as plane fitting depth reconstruction (PFitDR), involves cost computations on plane-fitted depth values over local segments. The second approach, which we call median filled depth reconstruction (MFillDR) is an even simpler method, wherein the reconstruction is carried out using computation of median of depth values over local segments. We demonstrate dense reconstruction from very less number of available depth points (e.g. as low as 1%). Our methods favorably compare with a recent related state-of-the-art method, both qualitatively as well as quantitatively in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices. © 2017 IEEE.Item GMM based single depth image super-resolution(Springer Verlag service@springer.de, 2018) Balure, C.S.; Ramesh Kini, M.R.; Bhavsar, A.Super-resolution (SR) is a technique to improve the resolution of an image from a sequence of input images or from a single image. As SR is an ill-posed inverse problem, it leads to many suboptimal solutions. Since modern depth cameras suffer from low-spatial resolution and are noisy, we present a Gaussian mixture model (GMM) based method for depth image super-resolution (SR). We train GMM from a set of high-resolution and low-resolution (HR-LR) synthetic training depth images to learn the relation between the HR and the LR patches in the form of covariance matrices. We use expectation-maximization (EM) algorithm to converge to an optimal solution. We show the promising results qualitatively and quantitatively in comparison to other depth image SR methods. © Springer Nature Singapore Pte Ltd. 2018.