GPU-Accelerated nick local image thresholding algorithm

M. Hassan Najafi, Anirudh Murali, David J. Lilja, John Sartori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Binarization plays an important role in document image processing, particularly in degraded document images. Among all local adaptive image thresholding algorithms, the Nick method has shown excellent binarization performance for degraded document images. However, local image thresholding algorithms, including the Nick method, are computationally intensive, requiring significant time to process input images. In this paper, we propose three CUDA GPU parallel implementations of the Nick local image thresholding algorithm for faster binarization of large images. Our experimental results show that the GPU-accelerated implementations of the Nick method can achieve up to 150x performance speedup on a GeForce GTX 480 compared to its optimized sequential implementation.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 21st International Conference on Parallel and Distributed Systems, ICPADS 2015
PublisherIEEE Computer Society
Number of pages9
ISBN (Electronic)9780769557854
StatePublished - Jan 15 2016
Event21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015 - Melbourne, Australia
Duration: Dec 14 2015Dec 17 2015

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
ISSN (Print)1521-9097


Other21st IEEE International Conference on Parallel and Distributed Systems, ICPADS 2015

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation grant no. CCF-1438286. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF

Publisher Copyright:
© 2015 IEEE.


  • CUDA GPU Programming
  • GPU acceleration
  • Image binarization
  • Image thresholding
  • Parallel programming


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