Abstract—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.
Index Terms—total variation regularization, noise removal method, bm3d method
Cite: Tsubasa Munezawa and Tomio Goto, "Noise Removal Method for Moving Images Using 3-D and Time-Domain Total Variation Regularization Decomposition," Journal of Image and Graphics, Vol. 7, No. 1, pp. 18-25, March 2019. doi: 10.18178/joig.7.1.18-25
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