Multi-view subspace clustering has attracted widespread attention for its superior clustering effectiveness. However, most of the current methods do not fully exploit the intra-view (sample-to-sample) and inter-view (view-to-view) information among multi-view data. Focusing on this problem, we propose a method called enhanced intra-inter view correlation learning for multi-view subspace clustering (EIVCL). EIVCL simultaneously considers the intra-view and inter-view correlations and introduces two constraint terms for each aspect to capture the data structure information. Specifically, in the intra-view space, we apply the tensor-singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on the tensor formed by stacking self-representative coefficient matrices for obtaining the high-order correlation information between samples in the specific view. Moreover, we introduce a hypergraph-induced Laplace regularization term to preserve the local geometric structure within views. In the inter-view space, we impose the t-SVD-based TNN on the rotated tensor to obtain the multiple views correlation. Furthermore, we utilize the kernel dependence metric, namely Hilbert–Schmidt independence criterion, to capture the high-order non-linear relationships between views. In addition, all above strategies are integrated into a unified clustering framework, which is solved by our proposed optimization algorithm based on the alternating direction method of multipliers. Extensive experiments on six benchmark datasets demonstrate that EIVCL outperforms several state-of-the-art multi-view algorithms.
Recently, a real objects-based full-color holographic display system usually uses a DSLR camera array or depth camera to collect data, and then relies on a spatial light modulator to modulate the input light source for the reconstruction of the 3D scene from the real objects. The main challenges faced by the holographic 3D display were introduced, including limited generation speed and accuracy of the computer-generated holograms, the imperfect performance of the holographic display system. In this research, we generated more effective and accurate point cloud data by developing a 3D saliency detection model in the acquisition module. Object points categorized into depth girds with identical depth values in the red, green, and blue (RGB) channels. In each channel, the depth girds are segmented into M × N parts, and only the effective area of the depth grids will be calculated. Computer-generated holograms (CGHs) are generated from efficient depth grids by using Fast Fourier transform (FFT). Compared to the wave-front recording plane (WRP) and traditional PCG methods, the computational complexity is dramatically reduced. The feasibility of the proposed approaches is established through experiments.
KEYWORDS: Holograms, 3D image reconstruction, Image quality, Wavefronts, Computer generated holography, Digital holography, Holography, 3D modeling, Image enhancement, RGB color model
In this paper, a uniform multiple wavefront recording planes (UM-WRPs) method for enhancing the image quality of the RGB-depth (RGB-D) image hologram is proposed. The conventional multiple wavefront recording planes (M-WRPs) based full-color computer-generated hologram (CGH) have color uniformity problem caused by intensity distribution. In order to solve the problem, the proposed method generates depth-related wavefront recording planes (WRPs) to enhance the color uniformity and accelerate hologram generation using a fixed active area. Compared with conventional MWRPs methods, the quality of reconstructed images of this method is improved significantly. The image improvement of the proposed method is confirmed by numerical reconstruction
Viewing angle of the conventional flat hologram is not very large (less than 180°) attributed to their planar observation surface. If we want to synthesize a wide view computer generated hologram, a numerical simulation of the diffraction on the non-planar observation surfaces is required, computer generated cylindrical hologram (CGCH) can be a solution. Approximately 2,500 object points were used for this research. We have realized a CGCH that is viewable in 360°. However, the heavy computation load is one of the issues. Therefore, we propose a fast calculation method for a computer generated cylindrical hologram by the use of wave-front recording surface. The wave-front recording surface is placed between the object data and a CGCH. When the wave-front recording surface is placed close to the object, the object light passes through a small region on the wave recording surface. Therefore the computational complexity for the object light is very small. We can obtain a CGCH to execute diffraction calculation from the wave-front recording surface, propagating the recorded optical field of the wave-front recording surface to the cylindrical hologram surface using only two FFT operations and hence is much faster.
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