{"title":"GPU-Accelerated Nick Local Image Thresholding Algorithm","authors":"M. Najafi, Anirudh Murali, D. Lilja, J. Sartori","doi":"10.1109/ICPADS.2015.78","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":231517,"journal":{"name":"2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS)","volume":"459 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPADS.2015.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
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.
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