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Eigen-CNN: Eigenimages Plus Eigennoise Level Maps Guided Network for Hyperspectral Image Denoising
Lina Zhuang, Michael K. Ng, Lianru Gao, Zhicheng Wang
In recent years, neural network-based methods have shown promising results in hyperspectral image (HSI) denoising areas. Real HSIs exhibit the substantial variations in noise distribution due to various factors, such as different imaging techniques, camera variations, imaging environments, and hardware aging. In this article, we develop an eigenimage plus eigennoise level map-guided convolutional neural network (CNN) for HSI denoising. Our main idea is to perform eigendecomposition on HSIs, utilize the low-rank property of HSIs in the spectral dimension and approximate the spectral vectors in a low-dimensional orthogonal subspace, where representation coefficients are called eigenimages. Besides eigenimages, we make use of estimated eigennoise level map as an input to guide the network for denoising. The proposed network can be constructed without restriction in the number of eigencomponents by using all eigenimages and eigennoise level maps of training noisy-clean pairs. In the inference part, the trained network can be used to remove noise in observed eigenimages without restriction in the number of eigencomponents, and an underlying clean image HSI can be estimated by performing orthogonal projection back. Experimental results on both simulated and real HSIs demonstrate the effectiveness of our trained Eigen-CNN compared with state-of-the-art HSI denoising methods. A MATLAB demo of this work is available at: https://github.com/LinaZhuang/HSI-denoiser-Eigen-CNN for the sake of reproducibility.
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