Eigenimage2Eigenimage (E2E): A Self-Supervised Deep Learning Network for Hyperspectral Image Denoising

• Published in: IEEE transaction on neural networks and learning systems Vol. PP; pp. 1 - 15
• Main Authors: Zhuang, Lina, Ng, Michael K., Gao, Lianru, Michalski, Joseph, Wang, Zhicheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 19.07.2023
• Subjects: Convolutional neural network; Deep learning; deep neural network; hyperspectral image (HSI) restoration; Hyperspectral imaging; low-rank representation; Noise measurement; Noise reduction; Self-supervised learning; subspace representation; Tensors; Training
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2023.3293328

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-Eigenimage2Eigenimagehttps://github.com/LinaZhuang/HSI-denoiser-Eigenimage2Eigenimage for the sake of reproducibility.