The hidden periodicity detection using a cyclic spectral function (CSF) is one of the well-known methods for cyclostationary signals analysis. Many two-dimensional signals (2D-signals), such as textures, can be considered cyclostationary or semi-cyclostationary. Applying such an assumption provides a new effective field for the analysis of 2D-signals. This paper presents two new algorithms for two-dimensional CSF (2D-CSF) estimation, namely two-dimensional tensor-based FFT accumulation method (2DT-FAM) and two-dimensional tensor-based strip spectral correlation analyzer (2DT-SSCA). These algorithms are fast and parallel. Moreover, they are based on tensor equations and linear-algebra that provide many advantages in the computational efficiency. Furthermore, the proposed schemes produce more information by preserving the information between pixels of an image using a two-dimensional window that improves classification accuracy and noise resistance property. To evaluate the performance of proposed algorithms, they are employed on two popular databases in texture analysis. 2DT-FAM as the unused promising texture analyzer and the new implementation of two-dimensional strip spectral correlation analyzer 2D-SSCA are compared with other state-of-the-art methods in terms of processing time, noise resistance, and classification accuracy. Experiment results show a 2% increase in correct classification rate and 10 times reduction in input feature dimensions in comparison with other studies and a 0.24 s decrease in processing times in comparison with the ordinary two-dimensional SSCA.

使用循环频谱函数（CSF）的隐藏周期性检测是用于循环平稳信号分析的众所周知的方法之一。可以将许多二维信号（2D信号）（例如纹理）视为循环平稳或半循环平稳的。应用这样的假设为2D信号的分析提供了一个新的有效领域。本文提出了两种用于二维CSF（2D-CSF）估计的新算法，即基于二维张量的FFT累积方法（2DT-FAM）和基于二维张量的带状谱相关分析器（2DT-SSCA）。这些算法是快速且并行的。而且，它们基于张量方程和线性代数，在计算效率方面提供了许多优势。此外，提出的方案通过使用二维窗口保留图像像素之间的信息，从而提高分类精度和抗噪性，从而产生更多信息。为了评估所提出算法的性能，将它们用于两个流行的数据库中的纹理分析。在处理时间，抗噪性和分类准确性方面，将2DT-FAM作为未使用的有希望的纹理分析器以及二维带状光谱相关分析器2D-SSCA的新实现与其他最新方法进行了比较。实验结果表明，与其他研究相比，正确分类率提高了2％，输入特征尺寸减少了10倍，与普通二维SSCA相比，处理时间减少了0.24 s。