Abstract
The aim of digital image processing is to improve the quality of image and subsequently to perform features extraction and classification. It is effectively used in computer vision, medical imaging, meteorology, astronomy, remote sensing and other related field. The main problem is that it is generally time consuming process; Parallel Computing provides an efficient and convenient way to address this issue. Main purpose of this review is to provide the comparative study of the existing contributions of implementing parallel image processing applications with their benefits and limitations. Another important aspect of this study is to provide the brief introduction of parallel computing and currently available parallel architecture, tools and techniques used for implementing parallel image processing. The aim is to discuss the problems encountered to implement parallel computing in various image processing applications. In this research we also tried to describe the role of parallel image processing in the field of medical imaging.
Highlights
Days, Image processing plays a very essential role in numerous fields for example optics, computer science, mathematics, surface physics and visual psychophysics in case of computer vision its applications include remote sensing, feature extraction, meteorology, face detection, finger-print detection, optical sorting, astronomy, argument reality, microscope imaging, lane departure warning system (Basavaprasad and Ravi, 2014)
This study summarizes existing parallel image processing techniques and tools implemented by different scientists and researchers
As we have discussed above that parallel computing is having very important significance in several image processing techniques like edge detection, histogram equalization, noise removal, image registration, image segmentation, feature extraction, different optimization techniques and many more
Summary
Image processing plays a very essential role in numerous fields for example optics, computer science, mathematics, surface physics and visual psychophysics in case of computer vision its applications include remote sensing, feature extraction, meteorology, face detection, finger-print detection, optical sorting, astronomy, argument reality, microscope imaging, lane departure warning system (Basavaprasad and Ravi, 2014). Parallel processing has become a significant tool for implementing high speed computing. This study summarizes existing parallel image processing techniques and tools implemented by different scientists and researchers. There are several application area of parallel computing image processing, Atmosphere, Earth, Environment, Applied Physics, Nuclear, condensed matter Computer Science, Mathematics, Electrical Engineering and Many more discussed in Barney (2014)and Slabaugh et al (2010). Node: It is an individual "computer in a box" It is comprised of numerous CPUs/Cores/Processors, network interfaces, memory, etc. Task: This is a logically distinct section of computational effort This is normally a program or set of commands which is executed by a core/processor. Shared Memory: As per hardware point of view, it is just like a computer architecture in which all cores/processors have straight access to regular physical memory. Distributed memory: For hardware point of view, it is just like a network based memory access used for Speedup
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