Design and implementation of a parallel object-oriented image processing toolkit

Advances in technology have enabled us to collect data from observations, experiments, and simulations at an ever increasing pace. As these data sets approach the terabyte and petabyte range, scientists are increasingly using semi-automated techniques from data mining and pattern recognition to find useful information in the data. In order for data mining to be successful, the raw data must first be processed into a form suitable for the detection of patterns. When the data is in the form of images, this can involve a substantial amount of processing on very large data sets. To help make this task more efficient, we are designing and implementing an object-oriented image processing toolkit that specifically targets massively-parallel, distributed-memory architectures. We first show that it is possible to use object-oriented technology to effectively address the diverse needs of image applications. Next, we describe how we abstract out the similarities in image processing algorithms to enable re-use in our software. We will also discuss the difficulties encountered in parallelizing image algorithms on the massively parallel machines as well as the bottlenecks to high performance. We will demonstrate our work using images from an astronomical data set, and illustrate how techniques such as filters and denoising through the thresholding of wavelet coefficients can be applied when a large image is distributed across several processors.

[1]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[2]  Hugh Garraway.  Parallel Computer Architecture: A Hardware/Software Approach , 1999, IEEE Concurrency.

[3]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[4]  A. Bruce,et al.  Understanding WaveShrink: Variance and bias estimation , 1996 .

[5]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[6]  George Karypis,et al.  Introduction to Parallel Computing , 1994 .

[7]  Ananth Grama,et al.  Data Mining: From Serendipity to Science - Guest Editors' Introduction , 1999, Computer.

[8]  Fionn Murtagh,et al.  Image Processing and Data Analysis - The Multiscale Approach , 1998 .

[9]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[10]  Ian T. Foster,et al.  Designing and building parallel programs - concepts and tools for parallel software engineering , 1995 .

[11]  Richard L. White,et al.  The FIRST Survey: Faint Images of the Radio Sky at twenty centimeters , 1995 .

[12]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[13]  Robert D. Falgout,et al.  Multigrid on massively parallel architectures , 2000 .

[14]  Geoffrey C. Fox,et al.  Parallel Computing Works , 1994 .

[15]  Sujit K. Ghosh,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 2001, Technometrics.

[16]  C. Kamath,et al.  Finding Bent-double Radio Galaxies: A Case Study in Data Mining , 2000 .

[17]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..