Three-dimensional （3D） acquisition has been the most important technique for augmented reality or virtual reality industries. Stereo vision can obtain 3D infor- mation of objects using disparity between two images c...Three-dimensional （3D） acquisition has been the most important technique for augmented reality or virtual reality industries. Stereo vision can obtain 3D infor- mation of objects using disparity between two images captured by a stereo camera. However, due to lack of per- spectives for 3D objects in this technique, accurate 3D information may not be generated and the number of viewing points is limited. By contrast, multi-view or super multi-view imaging techniques can obtain more accurate 3D information because they use multiple cameras for pickup of 3D objects so that more perspec- tives and viewing points of 3D objects can be recorded.展开更多
In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory...In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.展开更多
基金supported by the Technological Innovation R&D Program(No.S2405402)funded by the Small and Medium Business Administration(SMBA,Korea)
文摘Three-dimensional （3D） acquisition has been the most important technique for augmented reality or virtual reality industries. Stereo vision can obtain 3D infor- mation of objects using disparity between two images captured by a stereo camera. However, due to lack of per- spectives for 3D objects in this technique, accurate 3D information may not be generated and the number of viewing points is limited. By contrast, multi-view or super multi-view imaging techniques can obtain more accurate 3D information because they use multiple cameras for pickup of 3D objects so that more perspec- tives and viewing points of 3D objects can be recorded.
文摘In the medical computer tomography （CT） field, total variation （TV）, which is the l1-norm of the discrete gradient transform （DGT）, is widely used as regularization based on the compressive sensing （CS） theory. To overcome the TV model＇s disadvantageous tendency of uniformly penalizing the image gradient and over smoothing the low-contrast structures, an iterative algorithm based on the l0-norm optimization of the DGT is proposed. In order to rise to the challenges introduced by the l0-norm DGT, the algorithm uses a pseudo-inverse transform of DGT and adapts an iterative hard thresholding （IHT） algorithm, whose convergence and effective efficiency have been theoretically proven. The simulation demonstrates our conclusions and indicates that the algorithm proposed in this paper can obviously improve the reconstruction quality.
