Ultrasound Image Restoration Using Weighted Nuclear Norm Minimization

Hanmei Yang, Heng Zhang, Ye Luo, Jianwei Lu, Jian Lu

Abstract: Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent applications. The paper introduces a nonconvex low-rank matrix approximation model for ultrasound images restoration, which integrates the weighted nuclear norm minimization (WNNM) and data fidelity term. WNNM can adaptively assign weights on different singular values to preserve more details in restored images. The fidelity term about ultrasound images do not be utilized in existing low-rank ultrasound denoising methods. This optimization question can effectively solved by alternating direction method of multipliers (ADMM). The experimental results on simulated images and real medical ultrasound images demonstrate the excellent performance of the proposed method compared with other four state-of-the-art methods.