PDE-based Noise Removal with Geometrical Mean Diffusion of Adaptive TV and Gauss Curvature-driven Diffusion

摘要：

The adaptive TV model can keep sharp edges while remove noise. Some key structures that have low gradient magnitudes cannot be preserved by the adaptive TV model, and can be preserved by the Gauss curvature-driven diffusion model while removing noise that has a large Gauss curvature. However, one of the defects of the Gauss curvature-driven diffusion model is that it requires much iteration. Thus, a novel edge preserving model based on the geometrical mean value of the adaptive TV and the Gauss curvature is proposed in this paper. In the proposed model, the diffusion conductance is determined by combining the gradient and the Gauss curvature to preserve sharp edges and some key structures. Comparative results on two synthetic images and a medical image denoising demonstrate that the proposed model outperforms the Gauss curvature-driven diffusion model in terms of both mean square error (MSE) and peak signal-to-noise ratio (PSNR).

作者: Shufang Qiu Zewen Wang Biqin He

作者单位: School of Science East China Institute ofTechnology Nanchang, Jiangxi, China. 330013 School of Science East China Institute of Technology Nanchang, Jiangxi, China. 330013 Department of Mathematics East China Institute of Technology Fuzhou, Jiangxi, China. 344000

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 739-743

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）