Cakir Serdar, Uzeler Hande, Aytaç Tayfun
Appl Opt. 2013 Oct 1;52(28):6858-67. doi: 10.1364/AO.52.006858.
The compressive sensing (CS) framework states that a signal that has a sparse representation in a known basis may be reconstructed from samples obtained at a sub-Nyquist sampling rate. The Fourier domain is widely used in CS applications due to its inherent properties. Sparse signal recovery applications using a small number of Fourier transform coefficients have made solutions to large-scale data recovery problems, including image recovery problems, more practical. The sparse reconstruction of 2D images is performed using the sampling patterns generated by taking the general frequency characteristics of the images into account. In this work, instead of forming a general sampling pattern for infrared (IR) images, a special sampling pattern is obtained by gathering a database to extract the frequency characteristics of IR sea-surveillance images. Experimental results show that the proposed sampling pattern provides better sparse recovery results compared to the widely used patterns proposed in the literature. It is also shown that, together with a certain image dataset, the sampling pattern generated by the proposed scheme can be generalized for various image sparse recovery applications.
压缩感知（CS）框架指出，在已知基中具有稀疏表示的信号可以从以亚奈奎斯特采样率获得的样本中重建。由于其固有特性，傅里叶域在CS应用中被广泛使用。使用少量傅里叶变换系数的稀疏信号恢复应用使大规模数据恢复问题（包括图像恢复问题）的解决方案更加实用。二维图像的稀疏重建是通过考虑图像的一般频率特性生成的采样模式来执行的。在这项工作中，不是为红外（IR）图像形成一般的采样模式，而是通过收集数据库以提取IR海面监视图像的频率特性来获得特殊的采样模式。实验结果表明，与文献中提出的广泛使用的模式相比，所提出的采样模式提供了更好的稀疏恢复结果。还表明，与特定的图像数据集一起，所提出的方案生成的采样模式可以推广到各种图像稀疏恢复应用中。
