Abstract

Faber Schauder discrete wavelet transform (FSDWT) has many interesting advantages in image and video processing owing to its simplicity and its multiscale-based theory. It preserves the pixel ranges, has arithmetic operations, and detects edges in multiscale representation. With the increase of image size and the real-time requirement of many applications, the FSDWT computation becomes complex and needs other techniques to deal with it. To solve this problem, the FSDWT is implemented in parallel on a Graphics Processing Unit (GPU) using Compute Unified Device Architecture (CUDA) code. The results demonstrate that the GPU-based FSDWT exceedingly outperforms the CPU FSDWT.

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