Sampling theory for sparse signals and its applications to image super-resolution

研究了稀疏信号的多通道采样和基于该采样方案的图像特征提取。前者主要是解决在第一年，我们提出了两个算法，一个是所谓的MAP估计算法与信号的先验分布，另一个是l1范数最小化算法。在第二年，我们设计了一个精确的线边缘提取算法，可以应用于超分辨率。我们通过计算机模拟表明，所提出的方法是约10 dB的抗噪声比传统的方法。

We studied multi-channel sampling for sparse signals and image feature extraction using the sampling scheme. The former was mainly tackled in the first year, and we proposed two algorithms ; one is the so-called MAP estimation algorithm with a prior distribution of signals, and the other is the l1-norm minimization algorithm. In the second year, we devised an exact line-edge extraction algorithm that can be applied for super-resolution. We showed by computer simulations that the proposed method is approximately 10 dB better against noise than the conventional methods.