论文标题
压缩采样与基于各向异性网格的图像表示之间的初步比较
A Preliminary Comparison Between Compressive Sampling and Anisotropic Mesh-based Image Representation
论文作者
论文摘要
在过去的二十年中，压缩传感（CS）已成为一个流行的领域，以代表和重建稀疏信号，其样本少于信号本身。尽管常规图像本身并不稀疏，但在小波变换域中可以稀少地表示许多图像。因此，CS还广泛应用代表数字图像。但是，一种替代方法，即基于网格的图像表示（MBIR）等自适应采样，并没有引起太多关注。 MBIR直接在图像像素上工作，并使用三角形网格代表较少点的图像。在本文中，我们在CS和最近开发的MBIR方法AMA表示之间进行了初步比较。结果表明，在相同的样品密度下，AMA表示可以比基于测试算法的CS提供更好的重建质量。需要对最近的算法进行进一步研究以进行详尽的比较。
Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself. Although regular images are not sparse on their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. However, an alternative approach, adaptive sampling such as mesh-based image representation (MbIR), has not attracted as much attention. MbIR works directly on image pixels and represents the image with fewer points using a triangular mesh. In this paper, we perform a preliminary comparison between the CS and a recently developed MbIR method, AMA representation. The results demonstrate that, at the same sample density, AMA representation can provide better reconstruction quality than CS based on the tested algorithms. Further investigation with recent algorithms is needed to perform a thorough comparison.
