Fast parallel blur detection on GPU View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11-12

AUTHORS

Giang Son Tran, Thi Phuong Nghiem, Jean-Christophe Burie

ABSTRACT

Blur detection, a task to determine whether an image is blurred or not, is very helpful in various applications of image processing and computer vision. In this paper, we propose a novel method to accelerate blur detection algorithms based on Haar wavelet transform. The method decouples data dependency to gain fast 3-level Haar wavelet transform. With the obtained independence, the blur detection steps can be performed in parallel using native GPU thread blocks. We evaluated our proposed method on embedded devices, desktop and server. Our experiments show that on desktop and server, the proposed method obtains a huge performance speedup. On embedded devices, our GPU-based 3-level Haar wavelet transform is up to 4.9 times better performance and 4.3 times better power efficiency than CPU-based blur detection algorithms. More... »

PAGES

1-11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11554-018-0837-1

DOI

http://dx.doi.org/10.1007/s11554-018-0837-1

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https://app.dimensions.ai/details/publication/pub.1109858555


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