Computational Benefits, Limitations and Techniques of Parallel Image Processing

2020 ◽  
pp. 1-9
Author(s):  
Maha Heng Li ◽  
Marinka Yu Zhang

The main purpose of digitalized image processing is to enhance the quality of images and subsequently to facilitate the process of feature classification and extraction. This process is effectively utilized in healthcare imaging, computer visioning, astronomy, meteorology, and remote sensing among other essential fields. The main issue is that this technique takes a lot of time even though it provides convenient and efficient means of addressing the prevailing issue. In this paper, we shall provide a comparative evaluation of the present contributions adding to the application of parallel image processing including their limitations and benefits. Another key segment of research in this paper is to evaluate parallel computing, presently available techniques, tools and architecture in different image processing applications. As such, this paper is purposed to critically evaluate the role of parallel imaging in the field of healthcare imaging.

Author(s):  
SERGE MIGUET ◽  
JEAN-MARC PIERSON

The parallel implementation of image processing algorithms implies an important choice of data distribution strategy. In order to handle the specific constraints associated with images, data distribution must take into account not only the locality of the data and its geometrical regularity but also the possible irregular computation costs associated with different image elements. A widely studied field to tackle this problem is the family of methods related to rectilinear partitioning. We introduce two fully parallel heuristics that compute suboptimal partitions, with a better complexity than the best known algorithms that compute optimal partitions. In this paper, we compare our heuristics to an optimal partitioning, both in terms of execution time and accuracy of the partition. We give some theoretical bounds on the quality of these heuristics that are corroborated by results of random numerical experiments and real applications.


2014 ◽  
Author(s):  
Kevin Vincent ◽  
Damien Nguyen ◽  
Brian Walker ◽  
Thomas Lu ◽  
Tien-Hsin Chao

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