文将3D Context模型应用于 Surfacelet变换域 ,提出一种新的视频去噪方法. Surfacelet变换(ST)是一种

新的3D变换 ,具有多方向分解、 各向异性和低冗余度等性质. 根据视频信号 ST域内系数和噪声分布的特征 ,将 2D

Context 模型拓展到3D ,按照能量分布将 ST系数分成多个子块 ,每个子块有独立的能量和阈值估计.实验结果表明 ,本

文算法噪声抑制效果明显优于分层 2D去噪声方法和其它现有的 3D方法 ,去噪视频的 PSNR值提高了约 2dB.从视觉

效果来看 ,本文算法在去除噪声的同时 ,能很好的保留视频图像细节 ,运动物体非常平滑 ,有效解决传统算法中存在的

拖影、 闪烁等问题 ,尤其适合于包含剧烈运动和丰富纹理图像的视频.-　We propose a novel video denoising method with 3D Context Model in Surfacelet Transform Domain (3DCMST)

in this paper. In order to take advantage of the characteristic of the coefficients , the Context model was extended from 2D to 3D. The

ST coefficients were divided into several parts according to their energy distribution by 3D Context model and each part had inde2

pendent energy estimate and threshold. Experimental results show that the proposed method achieves better denoising performance

than other 3D or hierarchical 2D denoising methods , and remarkably improves the PSNR of video about 2dB. In terms of visual

quality , the proposed method can effectively preserve the video detail ,and the trajectory of motion object is very smooth ,which is

especially adequate to process the video frames with acute movement and plenty of texture.