Zhuang Lina, Ng Michael K, Gao Lianru, Michalski Joseph, Wang Zhicheng
IEEE Trans Neural Netw Learn Syst. 2024 Nov;35(11):16262-16276. doi: 10.1109/TNNLS.2023.3293328. Epub 2024 Oct 29.
The performance of deep learning-based denoisers highly depends on the quantity and quality of training data. However, paired noisy-clean training images are generally unavailable in hyperspectral remote sensing areas. To solve this problem, this work resorts to the self-supervised learning technique, where our proposed model can train itself to learn one part of noisy input from another part of noisy input. We study a general hyperspectral image (HSI) denoising framework, called Eigenimage2Eigenimage (E2E), which turns the HSI denoising problem into an eigenimage (i.e., the subspace representation coefficients of the HSI) denoising problem and proposes a learning strategy to generate noisy-noisy paired training eigenimages from noisy eigenimages. Consequently, the E2E denoising framework can be trained without clean data and applied to denoise HSIs without the constraint with the number of frequency bands. Experimental results are provided to demonstrate the performance of the proposed method that is better than the other existing deep learning methods for denoising HSIs. A MATLAB demo of this work is available at https://github.com/LinaZhuang/HSI-denoiser-Eigenimage2Eigenimage for the sake of reproducibility.
基于深度学习的去噪器的性能高度依赖于训练数据的数量和质量。然而，在高光谱遥感领域，成对的噪声-干净训练图像通常是不可用的。为了解决这个问题，这项工作采用了自监督学习技术，我们提出的模型可以自行训练，从噪声输入的另一部分学习噪声输入的一部分。我们研究了一种通用的高光谱图像（HSI）去噪框架，称为特征图像到特征图像（E2E），它将HSI去噪问题转化为特征图像（即HSI的子空间表示系数）去噪问题，并提出了一种学习策略，从噪声特征图像生成噪声-噪声成对训练特征图像。因此，E2E去噪框架可以在没有干净数据的情况下进行训练，并应用于对HSI进行去噪，而不受频带数量的限制。实验结果表明，所提方法的性能优于其他现有的用于HSI去噪的深度学习方法。为了便于重现，这项工作的MATLAB演示可在https://github.com/LinaZhuang/HSI-denoiser-Eigenimage2Eigenimage上获取。
