请简述信号去噪的算法发展历程，并给出参考资料

信号去噪的算法发展历程可以简单归纳为以下几个阶段：

1. 经典去噪方法：如滤波器、小波去噪等；
2. 基于稀疏性的去噪方法：如基于稀疏表示的去噪、基于稀疏编码的去噪等；
3. 基于机器学习的去噪方法：如基于神经网络的去噪、基于稀疏自编码器的去噪、基于卷积神经网络的去噪等；
4. 基于深度学习的去噪方法：如基于深度神经网络的去噪、基于生成对抗网络的去噪等。

参考资料：

1. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736-3745.
2. Yang, J., Wright, J., Huang, T., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 19(11), 2861-2873.
3. Zhang, K., Zuo, W., & Chen, Y. (2018). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 27(6), 3049-3064.
4. Luo, P., Wang, Z., & Tang, X. (2019). Joint blind image denoising and decompression. IEEE Transactions on Image Processing, 28(2), 806-817.
5. Lehtomaki, M., & Virtanen, T. (2020). A review of deep learning based noise reduction methods in speech signal processing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 730-744.