Optimized Laplacian image sharpening algorithm based on graphic processing unit

In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.

[1]  William J. Dally,et al.  The GPU Computing Era , 2010, IEEE Micro.

[2]  Ning Wang,et al.  Angiogram Images Enhancement Method Based on GPU , 2013 .

[3]  Heng Wang,et al.  Image spatial diffusion on GPUs , 2008, APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems.

[4]  Odemir M. Bruno,et al.  Characterization of nanostructured material images using fractal descriptors , 2012, 1212.2677.

[5]  Stefano Soatto,et al.  Really Quick Shift: Image Segmentation on a GPU , 2010, ECCV Workshops.

[6]  L. da F. Costa,et al.  An entropy-based approach to automatic image segmentation of satellite images , 2009, 0911.1759.

[7]  Danny Crookes,et al.  IAL: a parallel image processing programming language , 1990 .

[8]  Damien Vandembroucq,et al.  Laplacian transfer across a rough interface: numerical resolution in the conformal plane , 2005 .

[9]  H. Kiwata,et al.  Physical consideration of an image in image restoration using Bayes’ formula , 2012 .

[10]  Choujun Zhan,et al.  On the distributions of Laplacian eigenvalues versus node degrees in complex networks , 2010 .

[11]  Ashok Ghatol,et al.  Implementation of Parallel Image Processing Using NVIDIA GPU Framework , 2011 .

[12]  Bryan W. Scotney,et al.  Edge Detecting for Range Data Using Laplacian Operators , 2010, IEEE Transactions on Image Processing.

[13]  Piroz Zamankhan,et al.  Bubbles and solid structures in a vibrated bed of granular materials , 2011 .

[14]  Xin Wang,et al.  Laplacian Operator-Based Edge Detectors , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Yoshio Yanagihara,et al.  A Study on a Method of Effective Memory Utilization on GPU Applied for Neighboring Filter on Image Processing , 2012, SOCO 2012.

[16]  Howard Jay Siegel,et al.  Parallel Processing Approaches to Image Correlation , 1982, IEEE Transactions on Computers.

[17]  Xing-Yuan Wang,et al.  A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network , 2013 .

[18]  Carlos Malaga,et al.  The effects of nutrient chemotaxis on bacterial aggregation patterns with non-linear degenerate cross diffusion , 2013, 1301.5058.

[19]  Hans Knutsson,et al.  fMRI analysis on the GPU - Possibilities and challenges , 2012, Comput. Methods Programs Biomed..

[20]  Adrian Sheppard,et al.  Techniques for image enhancement and segmentation of tomographic images of porous materials , 2004 .

[21]  Zhang Hongyan,et al.  Real time detection of antibody-antigen interaction using a laser scanning confocal imaging-surface plasmon resonance system , 2012 .

[22]  Cheng Wang,et al.  Parallel data mining techniques on Graphics Processing Unit with Compute Unified Device Architecture (CUDA) , 2011, The Journal of Supercomputing.

[23]  Raphaël Couturier,et al.  Fine-tuned High-speed Implementation of a GPU-based Median Filter , 2014, J. Signal Process. Syst..

[24]  Jianming Wei,et al.  A parallel Monte Carlo method for population balance modeling of particulate processes using bookkeeping strategy , 2014 .

[25]  Li Xin,et al.  Speeding up the MATLAB complex networks package using graphic processors , 2011 .

[26]  Pierre-François Marteau,et al.  LNA: Fast Protein Structural Comparison Using a Laplacian Characterization of Tertiary Structure , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[27]  Ilker Kilic,et al.  Generalized ICM for image segmentation based on Tsallis statistics , 2012 .

[28]  Munesh Singh Chauhan,et al.  Fractal Image Compression Using Dynamically Pipelined GPU Clusters , 2012, SocProS.

[29]  Bao Shang-lian,et al.  A new level set model for cell image segmentation , 2011 .

[30]  Mohamad Adnan Al-Alaoui,et al.  Parallel edge detection based on digital differentiator approximation , 2013, 2013 Third International Conference on Communications and Information Technology (ICCIT).