Parallel implementation of wavelet-based image denoising on programmable PC-grade graphics hardware

Abstract

The intensive computation of Discrete Wavelet Transform (DWT) due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large size digital images and/or high-definition videos. Although various software-based acceleration solutions, such as the lifting scheme, have been devised 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, even for applications beyond graphics. This practice is commonly referred as General-purpose Computing on GPU (GPGPU). This paper presented a GPGPU framework with the corresponding parallel computing solution for waveletbased image denoising by using off-the-shelf consumer-grade programmable GPUs. This framework can be readily 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 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 However, the intensive computation of DWT due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large size digital images and/or high-definition videos. Although various software-based acceleration solutions, such as the lifting scheme, have been devised 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, even for applications beyond graphics. This practice is commonly referred as General-purpose Computing on GPU (GPGPU). This paper presented a GPGPU framework with the corresponding parallel computing solution for wavelet-based image denoising by using off-the-shelf consumer-grade programmable GPUs. This framework can be readily incorporated with different forms of DWT by customising the parameter of the wavelet kernel. Experiment results show that the framework...

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

“…In Figure 1, the image processed by [23,24,27] or [28] is more blurred because it loses much edge information. The image processed by [25] or [26] includes much noise which destroys the clarity of the image.…”

“…We compare denoising results with articles [23][24][25][26][27][28] from qualitative and quantitative aspects. The denoised (e) Article [25] (f) Article [26] (g) Article [27] (h) Article [28] (i) The new method (e) Article [25] (f) Article [26] (g) Article [27] (h) Article [28] (i) The new method (e) Article [25] (f) Article [26] (g) Article [27] (h) Article [28] (i) The new method images are shown in Figure 3 (the noise standard deviation = 30), and the comparison results of denoised index PSNR and SSIM are shown in Table 3.…”

Medical image processing on the GPU – Past, present and future

“…Pock et al (2008) developed a CUDA implementation in order to speed up the solution of such models, both in 2D and 3D. Wavelet based image denoising is also a popular choice, and has been accelerated by Su and Xu (2010). Gomersall et al (2011) instead performed deconvolution of ultrasound data with a spatially varying blur function, also requiring GPU acceleration.…”

Epicurus: A platform for the visualisation of forensic documents based on a linguistic approach

“…the freeware tool, Araucaria) but current mark-up schemes of argumentation lack both objectivity and linguistic sophistication. Building on the work of the project team (see, for example, [1], [2], [3], [4], [5] and [6]), we are developing a visualisation tool for a mark-up scheme based on Text World Theory, which is a model of human discourse processing developed in Linguistics.…”