Parallel Implementation of Wavelet-based Image Denoising on Programmable PC-grade Graphics Hardware

The Discrete Wavelet Transform (DWT) has been extensively used for image compression and denoising in the areas of image processing and computer vision. However, the intensive computation of DWT due to its multilevel data decomposition and reconstruction brings a bottleneck that drastically reduces its performance and implementations for real-time applications when processing large size digital images and/or high-definition videos. Although various software accelerated solutions, such as the lifting scheme, have been proposed and achieved a higher performance in general, the pure software accelerated DWT still struggle to cope with the demands from real-time and interactive applications. With the growing capacity and popularity of graphics hardware, personal computers (PCs) nowadays are often equipped with programmable Graphics Processing Units (GPUs) for graphics acceleration. The GPU offers a cost effective parallel data processing mechanism for operations on large amount of data from applications beyond graphics, known as General-purpose Computing on GPU (GPGPU). This paper presented a GPGPU framework and parallel computing solutions for wavelet based image denoising by using off-the-shelf consumer-level programmable GPUs. This framework can be easily incorporated with different forms of DWT by customising the parameter of the wavelet kernel. Experiment results show that the framework gains applicability in data parallelism and satisfaction performance in accelerating computations for wavelet-based denoising.