Increasing chip power density has brought application specific accelerator architectures to the forefront as an energy and area efficient solution. While GPGPU systems take advantage of specialized hardware to perform computationally intensive tasks faster than chip multiprocessor (CMP) systems, accelerators are hardware units that are designed to execute a specific application efficiently. Real-time ultrasound imaging applications require the removal of multiplicative noise while maintaining a steady frame-rate, and are good candidates to explore accelerator-based systems. In this paper, we propose and evaluate the architecture of an accelerator designed to improve performance of SRAD image enhancing algorithm. We compare the projected performance of the SRAD accelerator to software implementations on a multi-core CPU and a CPU+GPU system. The proposed architecture achieves higher throughput by eliminating redundant fetches from memory and by storing intermediate data locally. The speedup of the GPU is found to be 3.2x over the CPU, while the accelerator achieved a speedup of 24x. The area efficiency of the GPU and accelerator is up to 1.6x and 370x better than the CPU, respectively. In comparison with the CPU, we find that the energy consumed for operation on a single frame is found to be 1.5x lesser on the GPU and up to 580x lesser on the accelerator.