论文标题
快速重建四分之三抽样测量，使用经常性的局部关节稀疏反卷积和外推
Fast Reconstruction of Three-Quarter Sampling Measurements Using Recurrent Local Joint Sparse Deconvolution and Extrapolation
论文作者
论文摘要
最近，与相同数量的方形像素相比，通过使用不同定向的L形像素，通过使用不同定向的L形像素来提供图像传感器的图像传感器的图像质量提高。四分之三的采样传感器可以理解为传统的低分辨率传感器，其中每个正方形像素的一个象限是不透明的。在测量之后，可以使用适当的重建算法在两个空间维度的分辨率上重建数据，并在常规网格上重建两倍。对于这种重建，局部关节稀疏的反卷积和外推（L-JSDE）已显示出表现很好。作为缺点，L-JSDE需要每兆像素的长时间计算时间。在本文中，我们提出了一个更快的L-JSDE版本，称为Recurrent L-JSDE（RL-JSDE），该版本是L-JSDE的重新制定。对于合理的经常性测量模式，RL-JSDE在不牺牲图像质量的情况下为CPU和GPU提供了显着的加速。与L-JSDE相比，CPU和GPU分别实现了20倍和733倍的加速。
Recently, non-regular three-quarter sampling has shown to deliver an increased image quality of image sensors by using differently oriented L-shaped pixels compared to the same number of square pixels. A three-quarter sampling sensor can be understood as a conventional low-resolution sensor where one quadrant of each square pixel is opaque. Subsequent to the measurement, the data can be reconstructed on a regular grid with twice the resolution in both spatial dimensions using an appropriate reconstruction algorithm. For this reconstruction, local joint sparse deconvolution and extrapolation (L-JSDE) has shown to perform very well. As a disadvantage, L-JSDE requires long computation times of several dozen minutes per megapixel. In this paper, we propose a faster version of L-JSDE called recurrent L-JSDE (RL-JSDE) which is a reformulation of L-JSDE. For reasonable recurrent measurement patterns, RL-JSDE provides significant speedups on both CPU and GPU without sacrificing image quality. Compared to L-JSDE, 20-fold and 733-fold speedups are achieved on CPU and GPU, respectively.
