Premium
Parallel multi‐level 2D‐DWT on CUDA GPUs and its application in ring artifact removal
Author(s) -
Zhu Leqing,
Zhou Yadong,
Zhang Daxing,
Wang Dadong,
Wang Huiyan,
Wang Xun
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3559
Subject(s) - computer science , cuda , speedup , fast fourier transform , parallel computing , discrete wavelet transform , wavelet , graphics , transformation (genetics) , filter (signal processing) , computational science , algorithm , artificial intelligence , computer vision , wavelet transform , computer graphics (images) , biochemistry , chemistry , gene
Summary This paper presented two schemes of parallel 2D discrete wavelet transform (DWT) on Compute Unified Device Architecture graphics processing units. For the first scheme, the image and filter are transformed to spectral domain by using Fast Fourier Transformation (FFT), multiplied and then transformed back to space domain by using inverse FFT. For the second scheme, the image pixels are convolved directly with filters. Because there is no data relevance, the convolution for data points on different positions could be executed concurrently. To reduce data transfer, the boundary extension and down‐sampling are processed during data loading stage, and transposing is completed implicitly during data storage. A similar skill is adopted when parallelizing inverse 2D DWT. To further speed up the data access, the filter coefficients are stored in the constant memory. We have parallelized the 2D DWT for dozens of wavelet types and achieved a speedup factor of over 380 times compared with that of its CPU version. We applied the parallel 2D DWT in a ring artifact removal procedure; the executing speed was accelerated near 200 times compared with its CPU version. The experimental results showed that the proposed parallel 2D DWT on graphics processing units can significantly improve the performance for a wide variety of wavelet types and is promising for various applications. Copyright © 2015 John Wiley & Sons, Ltd.