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 in their own, many can be sparsely represented in wavelet transform domain. Therefore, CS has also been widely applied to represent digital images. An alternative approach, adaptive sampling such as mesh-based image representation (MbIR), however, has not attracted as much attention. MbIR works directly on image pixels and represent 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 are needed to perform a thorough comparison.

翻译：过去二十年来,压缩遥感(CS)已经成为一个流行的领域,可以代表和重建一个比信号本身样本少得多的微小信号。虽然普通图像本身并不稀少,但许多图像在波盘变换域中可以代表很少。因此,CS还被广泛用于代表数字图像。另一种方法,例如以网状为基础的图像表示法(MbIR)等适应性抽样,没有引起同等的注意。MbIR直接在图像像素上工作,以使用三角网格的更小的点代表图像。在本文中,我们对CS和最近开发的MbIR方法(AMA代表法)进行了初步比较。结果显示,在同一样本密度下,AMA代表法可以提供比以测试的算法为基础的CS更好的重建质量。为了进行彻底比较,需要用最近的算法进行进一步调查。