Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 647262 21 pages doi 2009 647262 Research Article Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences Application to Noise Reduction in X-Ray Image Sequences Vincent Auvray 1 2 Patrick Bouthemy 1 and Jean Lienard2 1INRIA Centre Rennes-Bretagne-Atlantique Campus universitaire de Beaulieu 35042 Rennes Cedex France 2 General Electric Healthcare 283 rue de la Miniere 78530 Buc France Correspondence should be addressed to Vincent Auvray Received 27 November 2008 Accepted 6 April 2009 Recommended by Lisimachos P. Kondi This paper is concerned with the estimation of the motions and the segmentation of the spatial supports of the different layers involved in transparent X-ray image sequences. Classical motion estimation methods fail on sequences involving transparent effects since they do not explicitly model this phenomenon. We propose a method that comprises three main steps initial blockmatching for two-layer transparent motion estimation motion clustering with 3D Hough transform and joint transparent layer segmentation and parametric motion estimation. It is validated on synthetic and real clinical X-ray image sequences. Secondly we derive an original transparent motion compensation method compatible with any spatiotemporal filtering technique. A direct transparent motion compensation method is proposed. To overcome its limitations a novel hybrid filter is introduced which locally selects which type of motion compensation is to be carried out for optimal denoising. Convincing experiments on synthetic and real clinical images are also reported. Copyright 2009 Vincent Auvray et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction .
