Median Based Parallel Steering Kernel Regression for Image Reconstruction

G Dada Khalandhar, V Sai Ram, M Srinivasa Rao, Lalith Srikanth C, Pallav Kumar Baruah, Balasubramanian S, R Raghunatha Sarma
Sri Satya Sai Institute of Higher Learning, India
Computer Science & Information Technology, Volume 3, Number 4, 2013
@article{khalandhar2013median,

   title={MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTION},

   author={Khalandhar, G Dada and Ram, V Sai and Rao, M Srinivasa and Srikanth, Lalith and Baruah, Pallav Kumar and Balasubramanian, S and Sarma, R Raghunatha},

   year={2013}

}

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Image reconstruction is a process of obtaining the original image from corrupted data. Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges (especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used. Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
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