A CUDA Based Implementation of Locally-and Feature-Adaptive Diffusion Based Image Denoising Algorithm

In this paper we introduce a parallel implementation of locally-and feature-adaptive diffusion based (LFAD) method for image denoising using NVIDIA CUDA framework and graphics processing units (GPUs). LFAD is a novel method for removing additive white Gaussian (AWG) noise in images reported to yield high quality denoised images [1]. It approaches each image region separately and uses different number of nonlinear anisotropic diffusion iterations for each region to attain best peak signal to noise ratio (PSNR). The inverse difference moment (IDM) feature is embedded into a modified diffusion function. As the method has attained highest performance in the class of advanced diffusion based methods and it is competitive with all the state-of-the-art methods, however computationally intensive when executed on the general purpose CPU. To improve the performance, we implemented using the CUDA computational framework. In order to minimize GPU kernel access to the global memory, we use shared memory and the texture memory per multiprocessor. The performance of the GPU implementation of the LFAD has been tested on the standard benchmark images. We demonstrate that with a single NVIDIA Tesla C2050 GPU we can expedite the sequential CPU implementation in most cases from 13 to 20 times.