Rank Minimization Method for Speckle Noise Removal in Ultrasound Images - Yan, Hui-Yin; Liu, Yu-Peng; Wang, He-Xian; Chen, Hao (Engineering letters, 2025)
Ultrasound image plays an important role in many medical applications. However, images acquired in ultrasound imaging system are always corrupted by a kind of speckle noise, which seriously affects the images' qualities. In this paper, by exploiting image nonlocal similarities, we establish a maximum a posteriori (MAP) estimation-based matrix rank minimization model for speckle noise reduction, and design an alternating proximal gradient algorithm to solve the nonconvex optimization model. The convergence of the alternating proximal gradient algorithm is analyzed and proved. A image denoising method is finally developed by using the rank minimization model and its solving algorithm. Numerical experiments illustrate that the proposed denoising method can outperform some state-of-the-art methods for speckle noise removal in images.