The main aim is to improvement in the quality of image, perform other operation, extraction of information and to classify the image while doing image processing. It is effectively used in computer, medical and other related fields. The main problem is that it is generally a time-consuming process; Parallel computing (parallelism) provides an efficient and convenient way to address this issue. There are many challenges in Image processing like Filtering, Restoration and classification etc. In addition, it is also a time-consuming process. The solution of these challenges is to use a parallel computing technique known as parallel image processing. The main focus of this paper is to review and to provide the comparative study of the existing contributions of tools and techniques of parallel image processing and analysis between different technique which are MATLAB, CUDA, BIONIC, Hadoop and GPU (graphic Processing Unit) along with limitation and advantage of these tools and techniques. In this review, we also tried to discuss the architecture of these parallel image processing techniques.
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