Nonlocal ultrasound image despeckling via improved statistics and rank constraint

Hanmei Yang, Jian Lu, Ye Luo, Guokai Zhang, Heng Zhang, Yiwen Liang, Jianwei Lu

Abstract: Ultrasound images are often contaminated by speckle noise during the acquisition process, which influences the performance of subsequent applications. Hence, it is necessary to design an effective algorithm for despeckling to obtain a clearer ultrasound image. According to the low-rank property of ultrasound images and the statistical property of similar image patch matrices, a nonlocal low-rank model with an improved data fidelity function (LRDF) is introduced in this paper, which integrates the weighted nuclear norm minimization (WNNM) and an improved data fidelity term. The advantage of WNNM is that it can adaptively assign weights on different singular values to preserve more details in restored images. The fidelity term deduced from log-compressed images fits better to ultrasonic data. We adopt the alternating direction method of multipliers (ADMM) to solve this nonconvex optimization problem. The experimental results on simulated images and real medical ultrasound images verify the reweighting strategy is helpful in this application and demonstrate the excellent performance of the proposed method compared with other five state-of-the-art methods.