Second order total generalized variation for speckle reduction in ultrasound images

Abstract

Image denoising is one of the most important issues in image processing. For removing the speckle noise in ultrasound images, researchers have proposed the minimization models based on the total variation (TV), which effectively preserve the sharp edges. But they simultaneously suffer form the undesired artifacts, such as the staircase effect. To overcome this shortcoming, we propose a convex model by combining with the total generalized variation (TGV) regularization for retaining the fine detail and reducing the staircase effect. Furthermore, we develop an alternating direction method of multiplier (ADMM) to solve the proposed model. Experimental results demonstrate that our model outperforms some state-of-the-art methods in terms of visual and quantitative measures.