多视点稀疏测量的图像绘制方法

为了减少所需采集的视频数据量, 基于图像绘制(Image-based rendering, IBR) 的前沿方法将稠密视点信息映射成压缩感知框架中的原始信号, 并将稀疏视点图像作为随机测量值, 但低维测量信号由所有稠密视点信息线性组合而成, 而稀疏视点图像仅仅来源于部分视点信息, 导致稀疏视点采集的图像与低维测量信号不一致. 本文提出利用间隔采样矩阵消除测量信号与稀疏视点图像位置之间的差异, 进而通过约束由测量矩阵和基函数构成的传感矩阵尽量满足有限等距性, 使得能够获得原始信号的唯一精确解. 仿真实验结果表明, 相比于前沿方法, 本文提出的方法对于不同复杂程度的场景重建都提高了主客观质量.

Multi-view Sparse Measurement for Image-based Rendering Method

In order to reduce the amount of video data that needs to be acquired greatly, state-of-the-art of the image-based rendering (IBR) method maps the dense viewpoint information into the original signal in the compressed sensing frame and utilizes sparse viewpoint images as random measurement information. However, the low-dimensional measurement signals are linearly combined using all of the dense viewpoint information, and the sparse viewpoint images only originate from partial viewpoint information, which results in the images acquired by the sparse viewpoints are inconsistent with the low-dimensional measurement signal. A sparse viewpoint measurement matrix is proposed, and an interval sampling matrix is used to align the sampling positions between the measured values and sparse viewpoint image information. Then, we constrain the sensing matrix, which consists of the measurement matrix and basis function, to satisfy the restricted isometry property as much as possible. Finally, the unique solution of the original signal can be obtained. The simulation results show that compared with conventional methods, the proposed method improves the subjective and objective quality for scene reconstruction with difierent levels of complexity.