State-based Gauss-Seidel framework for real-time 2D ultrasound image sequence denoising on GPUs

The ultrasound image sequences are not only majorly contaminated by multiplicative noises but they are also usually contaminated with additive noises. As in the past few decades, there were some works, which had focused on removing the noises from ultrasound images, such as in the JY model [1] and in the variational model, which were able to remove both types of noises. However, denoising these noises from the ultrasound image sequence is a time-consuming process that occurred from using fixed-point iterative method. From our investigation, the most time-consuming process part of the denoising process is the Gauss- Seidel. By parallelizing these processes with modern multi-core and many-core processor, the denoising ultrasound image in real-time is possible. In this study, we propose the modified strategy from [2] for managing threads and propose the modified state-based Gauss-Seidel method from [16] for GPUs. Our proposed model can retain the frame order, and get the satisfactory frame rate (about 23.33 fps). The proposed strategy boosts the speedup of the frame denoising to 13.80 times compare to the sequential computation. © 2014 SERSC.