Noise Removal Method for Moving Images Using 3-D and Time-Domain Total Variation Regularization Decomposition
Noise Removal Method for Moving Images Using 3-D and Time-Domain Total Variation Regularization Decomposition
复制标题
利用3维和时域全变分正则化分解的运动图像去噪方法
DOI：
10.18178/joig.7.1.18-25
复制
发表时间：
2019
影响因子：
--
通讯作者：
T. Goto
中科院分区：
文献类型：
--
作者：
Tsubasa Munezawa;T. Goto
- 近年来，为了显示下一代显示器的高视觉广播，需要用于提高图像分辨率的超分辨率技术。此外，随着数码相机和智能手机的普及，人们有更多的机会处理相机图像。特别地，要求监视摄像机的图像通过去除噪声来获得高清晰度输出。针对超分辨率图像处理中存在的噪声混杂问题，提出了一种在超分辨率图像处理前去除噪声的方法。在我们提出的方法中，总变分正则化，这是分解为结构和纹理分量，在时间轴方向上扩展。结果，运动图像可以被分解为结构运动图像和纹理运动图像。理论上，认为具有大的总变差值的噪声分量应该转移到纹理分量。此外，我们的目标是分离的纹理成分和噪声，并旨在获取高清晰度的运动图像。我们通过与BM 3D方法进行比较来验证我们所提出的方法的性能，BM 3D方法被认为是运动图像噪声去除处理的最高性能。
— In recent years, in order to display high vision broadcast the next generation displays, super resolution techniques for improving image resolution are demanded. In addition, with the spread of digital cameras and smartphones, people have more opportunities to handle camera images. In particular, images of surveillance cameras are required to obtain high-definition output by removing noise. In this paper, in order to avoid the adverse effect of image quality deterioration when emphasizing noise mixed image which is a problem of super resolution processing, we examine a noise removal method before super resolution processing. In our proposed method, Total Variation regularization, which is decomposed into structure and texture components, is extended in direction of time axis. As a result, moving images can be decomposed into structure moving images and texture moving images. In theory, it is thought that noise components with large value of Total Variation should shift to texture components. Furthermore, we aim for separation of texture components and noise, and aim for acquisition of high-definition moving images. We verify the performance of our proposed method by comparing it with the BM3D method, which is regarded as the highest performance for moving image noise removal processing.