In recent years, neural network-based methods have shown promising results in hyperspectral image (HSI) denoising area. Real HSIs exhibit substantial variations in noise distribution due to various factors such as different imaging techniques, camera variations, imaging environments, and hardware aging. In this paper, we develop an eigenimage plus eigennoise level map guided convolutional neural network 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.

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.

The Plug-and-play (PnP) technique enables us to plug image priors into an ADMM framework for solving a regularized optimization problem. Deep image priors have shown their flexibility and robustness in solving several image inverse problems. Hyperspectral image (HSI) super-resolution problem is an ill-posed inverse problem that aims to obtain a high-resolution HSI (HR-HSI) by combining the information of low-resolution HSI (LR-HSI) and HR multispectral image simultaneously. This paper proposes a hyperspectral and multispectral image fusion framework termed E2E-fusion, plugged with a self-supervised deep learning prior calledEigenimage2Eigenimage. Firstly, the spectral low-rank structure of HSIs is exploited via subspace representations of spectra vectors. Meanwhile, benefiting from the high quality of the first eigenimage (i.e., representation coefficients), we design a self-supervised deep eigenimage guidance network image prior, E2E. By using the PnP technique, we plugged the E2E prior into the ADMM fusion framework to update the optimal objective function iteratively. The numerical experimental results both on the simulated datasets and real datasets demonstrate that the proposed method performs better than state-of-the-art fusion methods.

Hyperspectral images (HSIs) exist in directional stripes commonly due to the failure of pushbroom acquisition. These stripes are not only vertically and horizontally oriented but also tend to be oblique. Furthermore, they can also be aperiodic and heavy. To address this problem, we propose a hyperspectral destriping algorithm, namely, GF-destriping. Taking advantage of the high sparsity and strong directionality of stripes in HSIs, Gabor filters are used to detect the stripes band by band first, and then, an advanced inpainting method, FastHyIn, is used to recover to the striped image. The numerical experiments on simulated data and real data sets show that our proposed algorithm is efficient and superior to state-of-the-art HSI destriping algorithms.

Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-order tensor, which can perfectly retain the spatial information in the image. In this article, we extend the linear tensor method to the nonlinear tensor method and propose a nonlinear low-rank tensor unmixing algorithm to solve the generalized bilinear model (GBM). Specifically, the linear and nonlinear parts of the GBM can both be expressed as tensors. Furthermore, the low-rank structures of abundance maps and nonlinear interaction abundance maps are exploited by minimizing their nuclear norm, thus taking full advantage of the high spatial correlation in HSIs. Synthetic and real-data experiments show that the low rank of abundance maps and nonlinear interaction abundance maps exploited in our method can improve the performance of the nonlinear unmixing. A MATLAB demo of this work will be available athttps://github.com/LinaZhuangfor the sake of reproducibility.

Recently, deep learning-based denoising methods for hyperspectral images (HSIs) have been comprehensively studied and achieved impressive performance because they can effectively extract complex and nonlinear image features. Compared with deep learning-based methods, the nonlocal similarity-based denoising methods are more suitable for images containing edges or regular textures. We propose a powerful HSI denoising method, termed NL-3DCNN, combining traditional machine learning and deep learning techniques. NL-3DCNN exploits the high spectral correlation of an HSI by using subspace representation and corresponding representation coefficients are termed eigenimages. The high spatial correlation in eigenimages is exploited by grouping nonlocal similar patches, which are denoised by a 3D convolutional neural network. The numerical and graphical denoising results of simulated and real data show that the proposed method is superior to state-of-the-art methods.