Parallel implementation of Sobel filter using CUDA

Efficient solutions must be considered, in order to solve the problem of intensive computing of the image processing applications and to achieve high real-time performance. The graphics processing unit (GPU) is an effective and the most recent method used for accelerating extensive calculation algorithms to reduce the execution time by exploiting the power of parallel programming techniques and to obtain the highest performance. In this paper, we present a parallel GPU implementation of an edge detection algorithm with a Sobel operator using CUDA (Compute Unifies Architecture) environment. Furthermore, we analyze and prove the high performance of GPU implementation, by testing the algorithm on a standard central processing unit (CPU) to compare the computational efficiency of these systems. Our experimental results show that the effectiveness of the GPU implementation by its higher performances compared to sequential calculation.

[1]  Zhiyi Yang,et al.  Parallel Image Processing Based on CUDA , 2008, 2008 International Conference on Computer Science and Software Engineering.

[2]  Zhang Zhiping,et al.  Defects’ geometric feature recognition based on infrared image edge detection , 2014 .

[3]  Shivashankar J. Bhutekar,et al.  Parallel face Detection and Recognition on GPU , 2014 .

[4]  Cuneyt Akinlar,et al.  Edge Drawing: A combined real-time edge and segment detector , 2012, J. Vis. Commun. Image Represent..

[5]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Zhang Jin-Yu,et al.  Edge detection of images based on improved Sobel operator and genetic algorithms , 2009, 2009 International Conference on Image Analysis and Signal Processing.

[7]  Daniel Díaz-Pernil,et al.  Segmenting images with gradient-based edge detection using Membrane Computing , 2013, Pattern Recognit. Lett..

[8]  Justin P. Haldar,et al.  Accelerating advanced MRI reconstructions on GPUs , 2008, J. Parallel Distributed Comput..

[9]  Steve Mann,et al.  OpenVIDIA: parallel GPU computer vision , 2005, ACM Multimedia.

[10]  Scott B. Baden,et al.  A software-based dynamic-warp scheduling approach for load-balancing the Viola-Jones face detection algorithm on GPUs , 2013, J. Parallel Distributed Comput..

[11]  Yuan Zhang,et al.  A parallel adaptive segmentation method based on SOM and GPU with application to MRI image processing , 2016, Neurocomputing.

[12]  Jos B. T. M. Roerdink,et al.  Accelerating Wavelet Lifting on Graphics Hardware Using CUDA , 2011, IEEE Transactions on Parallel and Distributed Systems.

[13]  Hayat Al-Dmour,et al.  Quality optimized medical image information hiding algorithm that employs edge detection and data coding , 2016, Comput. Methods Programs Biomed..