A Parallelized Approach for Colorizing Grayscale Images
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-10 |
||
| Year of Publication : 2019 | ||
| Authors : Poorna Banerjee Dasgupta | ||
| DOI : 10.14445/22312803/IJCTT-V67I10P112 |
MLA Style:Poorna Banerjee Dasgupta"A Parallelized Approach for Colorizing Grayscale Images," International Journal of Computer Trends and Technology 67.10 (2019):69-72.
APA Style Poorna Banerjee Dasgupta. A Parallelized Approach for Colorizing Grayscale Images International Journal of Computer Trends and Technology, 67(10),69-72.
Abstract
Image processing plays an integral role in the field of computer science and is incorporated in numerous applications such as remote sensing, medical imaging, image restoration, and machine vision. One of the challenging tasks in image processing is colorizing grayscale images. Restoring color in grayscale images aids in enhancing the visual cognition of image data such medical images, surveillance images, and scientific illustrations. Compared to the relatively simpler process of converting color images to grayscale, the process of colorizing grayscale images is much more complex, is not based on any one particular method, and is significantly dependent on the human perception of color. Additionally, such a colorizing process is generally computation-intensive and tedious. Hence, a parallelized high-performance approach based on many-core programming and the CUDA platform is presented in this paper for colorizing grayscale images. The proposed approach has been developed with the objective of incorporation in huge image datasets (such as those obtained from satellites) and video frame sequences.
Reference
[1] Jeremy Nathans, Darcy Thomas, and David S Hogness,Molecular Genetics of Human Color Vision: The Genes Encoding Blue, Green, and Red Pigments, Science. 232(4747): 193–202, 1986.
[2] Michael W. Schwarz, William B. Cowan, and John C. Beatty,An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models, ACM Transactions on Graphics. 6 (2): 123–158, 1987.
[3] Rafael C. Gonzalez and Richard E. Woods,Digital Image Processing, Third Edition, Pearson Publications, 2009.
[4] Poorna B. Dasgupta and Amit Dave,GPGPU Based Parallelized Client-Server Framework for Providing High Performance Computation Support, International Journal of Computer Science and Technology, Volume 4, Issue 1, 2013.
[5] Kirk David B. and Wen-mei W. Hwu, Programming Massively Parallel Processors - A Hands-on Approach, Morgan Kaufman Publishers, USA, 2010.
[6] Nicolas Seiller, Nitin Singhal, and Kyu Park,Object Oriented Framework For Real-Time Image Processing On GPU, Proceedings of 2010 IEEE 17th International Conference on Image Processing September 26-29, 2010, Hong Kong.
[7] Ruye Wang. (2014) Conversion from RGB to HSI. [Online]. Available: http://fourier.eng.hmc.edu/e161/lectures/ColorProcessing
[8] (2013) Image Processing. [Online]. Available: https://www.imageeprocessing.com/
[9] Jian-Feng Li, Kaun-Quan Wang, and D. Zhang,A new equation of saturation in RGB-to-HSI conversion for more rapidity of computing, Proceedings of International Conference on Machine Learning and Cybernetics, IEEE, 2003.
[10] Kazuya Yoshinari, Kota Murahira, Yoshikatsu Hoshi, and Akira Taguchi,Color image enhancement in improved HSI color space, IEEE International Symposium on Intelligent Signal Processing and Communication Systems, 2013.
[11] Benjamin Corey. (2019) Science and Nature. [Online]. Available: https://outschool.com/
Keywords
image colorization, image processing, many-core programming, parallel processing.