GPU-based anisotropic diffusion algorithm for video image denoising

Image filtering is the process of removing noise which perturbs image analysis methods. In some applications like segmentation, denoising is intended to smooth homogeneous areas while preserving the contours. Real-time denoising is required in a lot of applications like image-guided surgical interventions, video analysis and visual serving. This paper presents an anisotropic diffusion method named the Oriented Speckle Reducing Anisotropic Diffusion (OSRAD) filter. The OSRAD works very well for denoising images with speckle noise. However, this filter has a powerful computational complexity and is not suitable for real time implementation. The purpose of this study is to decrease the processing time implementation of the OSRAD filter using a parallel processor through the optimization of the graphics processor unit. The results show that the suggested method is very effective for real-time video processing. This implementation yields a denoising video rate of 25 frames per second for 128 × 128 pixels. The proposed model magnifies the acceleration of the image filtering to 30 ×  compared to the standard implementation of central processing units (CPU). A quantitative comparison measure is given by parameters like the mean structural similarity index, the peak signal-to-noise ratio and the figure of merit. The modified filter is faster than the conventional OSRAD and keeps a high image quality compared to the bilateral filter and the wavelet transformation.