Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning

Supervise-Assisted Self-Supervised Deep-Learning Method for Hyperspectral Image Restoration

Hyperspectral image (HSI) restoration is a challenging research area, covering a variety of inverse problems. Previous works have shown the great success of deep learning in HSI restoration. However, facing the problem of distribution gaps between training HSIs and target HSI, those data-driven methods falter in delivering satisfactory outcomes for the target HSIs. In addition, the degradation process of HSIs is usually disturbed by noise, which is not well taken into account in existing restoration methods. The existence of noise further exacerbates the dissimilarities within the data, rendering it challenging to attain desirable results without an appropriate learning approach. To track these issues, in this article, we propose a supervise-assisted self-supervised deep-learning method to restore noisy degraded HSIs. Initially, we facilitate the restoration network to acquire a generalized prior through supervised learning from extensive training datasets. Then, the self-supervised learning stage is employed and utilizes the specific prior of the target HSI. Particularly, to restore clean HSIs during the self-supervised learning stage from noisy degraded HSIs, we introduce a noise-adaptive loss function that leverages inner statistics of noisy degraded HSIs for restoration. The proposed noise-adaptive loss consists of Stein's unbiased risk estimator (SURE) and total variation (TV) regularizer and fine-tunes the network with the presence of noise. We demonstrate through experiments on different HSI tasks, including denoising, compressive sensing, super-resolution, and inpainting, that our method outperforms state-of-the-art methods on benchmarks under quantitative metrics and visual quality. The code is available at https://github.com/ying-fu/SSDL-HSI.

Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution

Fusion-based spectral super-resolution aims to yield a high-resolution hyperspectral image (HR-HSI) by integrating the available high-resolution multispectral image (HR-MSI) with the corresponding low-resolution hyperspectral image (LR-HSI). With the prosperity of deep convolutional neural networks, plentiful fusion methods have made breakthroughs in reconstruction performance promotions. Nevertheless, due to inadequate and improper utilization of cross-modality information, the most current state-of-the-art (SOTA) fusion-based methods cannot produce very satisfactory recovery quality and only yield desired results with a small upsampling scale, thus affecting the practical applications. In this article, we propose a novel progressive spatial information-guided deep aggregation convolutional neural network (SIGnet) for enhancing the performance of hyperspectral image (HSI) spectral super-resolution (SSR), which is decorated through several dense residual channel affinity learning (DRCA) blocks cooperating with a spatial-guided propagation (SGP) module as the backbone. Specifically, the DRCA block consists of an encoding part and a decoding part connected by a channel affinity propagation (CAP) module and several cross-layer skip connections. In detail, the CAP module is customized by exploiting the channel affinity matrix to model correlations among channels of the feature maps for aggregating the channel-wise interdependencies of the middle layers, thereby further boosting the reconstruction accuracy. Additionally, to efficiently utilize the two cross-modality information, we developed an innovative SGP module equipped with a simulation of the degradation part and a deformable adaptive fusion part, which is capable of refining the coarse HSI feature maps at pixel-level progressively. Extensive experimental results demonstrate the superiority of our proposed SIGnet over several SOTA fusion-based algorithms.

Exploring the Spectral Prior for Hyperspectral Image Super-Resolution

In recent years, many single hyperspectral image super-resolution methods have emerged to enhance the spatial resolution of hyperspectral images without hardware modification. However, existing methods typically face two significant challenges. First, they struggle to handle the high-dimensional nature of hyperspectral data, which often results in high computational complexity and inefficient information utilization. Second, they have not fully leveraged the abundant spectral information in hyperspectral images. To address these challenges, we propose a novel hyperspectral super-resolution network named SNLSR, which transfers the super-resolution problem into the abundance domain. Our SNLSR leverages a spatial preserve decomposition network to estimate the abundance representations of the input hyperspectral image. Notably, the network acknowledges and utilizes the commonly overlooked spatial correlations of hyperspectral images, leading to better reconstruction performance. Then, the estimated low-resolution abundance is super-resolved through a spatial spectral attention network, where the informative features from both spatial and spectral domains are fully exploited. Considering that the hyperspectral image is highly spectrally correlated, we customize a spectral-wise non-local attention module to mine similar pixels along spectral dimension for high-frequency detail recovery. Extensive experiments demonstrate the superiority of our method over other state-of-the-art methods both visually and metrically. Our code is publicly available at https://github.com/HuQ1an/SNLSR.

Learning to Recover Spectral Reflectance From RGB Images

This paper tackles spectral reflectance recovery (SRR) from RGB images. Since capturing ground-truth spectral reflectance and camera spectral sensitivity are challenging and costly, most existing approaches are trained on synthetic images and utilize the same parameters for all unseen testing images, which are suboptimal especially when the trained models are tested on real images because they never exploit the internal information of the testing images. To address this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy that fine-tunes the well-trained network parameters with each testing image to combine external with internal information. To the best of our knowledge, this is the first work that successfully adapts the MAXL strategy to this problem. Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis. Besides, since the spectral reflectance of a scene is independent to its illumination while the corresponding RGB images are not, we recover the spectral reflectance of a scene from its RGB images captured under multiple illuminations to further reduce the unknown. Qualitative and quantitative evaluations demonstrate the effectiveness of our proposed network and of the MAXL. Our code and data are available at https://github.com/Dong-Huo/SRR-MAXL.

Bayesian Nonlocal Patch Tensor Factorization for Hyperspectral Image Super-Resolution

The synthesis of high-resolution (HR) hyperspectral image (HSI) by fusing a low-resolution HSI with a corresponding HR multispectral image has emerged as a prevalent HSI super-resolution (HSR) scheme. Recent researches have revealed that tensor analysis is an emerging tool for HSR. However, most off-the-shelf tensor-based HSR algorithms tend to encounter challenges in rank determination and modeling capacity. To address these issues, we construct nonlocal patch tensors (NPTs) and characterize low-rank structures with coupled Bayesian tensor factorization. It is worth emphasizing that the intrinsic global spectral correlation and nonlocal spatial similarity can be simultaneously explored under the proposed model. Moreover, benefiting from the technique of automatic relevance determination, we propose a hierarchical probabilistic framework based on Canonical Polyadic (CP) factorization, which incorporates a sparsity-inducing prior over the underlying factor matrices. We further develop an effective expectation-maximization-type optimization scheme for framework estimation. In contrast to existing works, the proposed model can infer the latent CP rank of NPT adaptively without tuning parameters. Extensive experiments on synthesized and real datasets illustrate the intrinsic capability of our model in rank determination as well as its superiority in fusion performance.

Unmixing Guided Unsupervised Network for RGB Spectral Super-Resolution

Spectral super-resolution has attracted research attention recently, which aims to generate hyperspectral images from RGB images. However, most of the existing spectral super-resolution algorithms work in a supervised manner, requiring pairwise data for training, which is difficult to obtain. In this paper, we propose an Unmixing Guided Unsupervised Network (UnGUN), which does not require pairwise imagery to achieve unsupervised spectral super-resolution. In addition, UnGUN utilizes arbitrary other hyperspectral imagery as the guidance image to guide the reconstruction of spectral information. The UnGUN mainly includes three branches: two unmixing branches and a reconstruction branch. Hyperspectral unmixing branch and RGB unmixing branch decompose the guidance and RGB images into corresponding endmembers and abundances respectively, from which the spectral and spatial priors are extracted. Meanwhile, the reconstruction branch integrates the above spectral-spatial priors to generate a coarse hyperspectral image and then refined it. Besides, we design a discriminator to ensure that the distribution of generated image is close to the guidance hyperspectral imagery, so that the reconstructed image follows the characteristics of a real hyperspectral image. The major contribution is that we develop an unsupervised framework based on spectral unmixing, which realizes spectral super-resolution without paired hyperspectral-RGB images. Experiments demonstrate the superiority of UnGUN when compared with some SOTA methods.

Hyperspectral image super-resolution via spectral matching and correction

Fusing a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution RGB image (HR-RGB) is an important technique for HR-HSI obtainment. In this paper, we propose a dual-illuminance fusion-based super-resolution method consisting of spectral matching and correction. In the spectral matching stage, an LR-HSI patch is first searched for each HR-RGB pixel; with the minimum color difference as a constraint, the matching spectrum is constructed by linear mixing the spectrum in the HSI patch. In the spectral correlation stage, we establish a polynomial model to correct the matched spectrum with the aid of the HR-RGBs illuminated by two illuminances, and the target spectrum is obtained. All pixels in the HR-RGB are traversed by the spectral matching and correction process, and the target HR-HSI is eventually reconstructed. The effectiveness of our method is evaluated on three public datasets and our real-world dataset. Experimental results demonstrate the effectiveness of our method compared with eight fusion methods.

GuidedNet: A General CNN Fusion Framework via High-Resolution Guidance for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution (HISR) is about fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI). Recently, convolutional neural network (CNN)-based techniques have been extensively investigated for HISR yielding competitive outcomes. However, existing CNN-based methods often require a huge amount of network parameters leading to a heavy computational burden, thus, limiting the generalization ability. In this article, we fully consider the characteristic of the HISR, proposing a general CNN fusion framework with high-resolution guidance, called GuidedNet. This framework consists of two branches, including 1) the high-resolution guidance branch (HGB) that can decompose the high-resolution guidance image into several scales and 2) the feature reconstruction branch (FRB) that takes the low-resolution image and the multiscaled high-resolution guidance images from the HGB to reconstruct the high-resolution fused image. GuidedNet can effectively predict the high-resolution residual details that are added to the upsampled HSI to simultaneously improve spatial quality and preserve spectral information. The proposed framework is implemented using recursive and progressive strategies, which can promote high performance with a significant network parameter reduction, even ensuring network stability by supervising several intermediate outputs. Additionally, the proposed approach is also suitable for other resolution enhancement tasks, such as remote sensing pansharpening and single-image super-resolution (SISR). Extensive experiments on simulated and real datasets demonstrate that the proposed framework generates state-of-the-art outcomes for several applications (i.e., HISR, pansharpening, and SISR). Finally, an ablation study and more discussions assessing, for example, the network generalization, the low computational cost, and the fewer network parameters, are provided to the readers. The code link is: https://github.com/Evangelion09/GuidedNet.

LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening

Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap. Different from previous variational methods, we first formulate a pioneering image super-resolution (ISR) degradation model, which equivalently removes the downsampling operator and transforms the tensor completion framework. Under such a framework, the original pansharpening problem is realized by the LRTC-based technique with some deblurring regularizers. From the perspective of regularizer, we further explore a local-similarity-based dynamic detail mapping (DDM) term to more accurately capture the spatial content of the panchromatic image. Moreover, the low-tubal-rank property of multispectral images is investigated, and the low-tubal-rank prior is introduced for better completion and global characterization. To solve the proposed LRTCFPan model, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Comprehensive experiments at reduced-resolution (i.e., simulated) and full-resolution (i.e., real) data exhibit that the LRTCFPan method significantly outperforms other state-of-the-art pansharpening methods. The code is publicly available at: https://github.com/zhongchengwu/code_LRTCFPan.

Deep Posterior Distribution-Based Embedding for Hyperspectral Image Super-Resolution

In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatial-spectral information of HS images efficiently and effectively. Specifically, in contrast to existing methods adopting empirically-designed network modules, we formulate HS embedding as an approximation of the posterior distribution of a set of carefully-defined HS embedding events, including layer-wise spatial-spectral feature extraction and network-level feature aggregation. Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable, producing PDE-Net, in which high-resolution (HR) HS images are iteratively refined from the residuals between input low-resolution (LR) HS images and pseudo-LR-HS images degenerated from reconstructed HR-HS images via probability-inspired HS embedding. Extensive experiments over three common benchmark datasets demonstrate that PDE-Net achieves superior performance over state-of-the-art methods. Besides, the probabilistic characteristic of this kind of networks can provide the epistemic uncertainty of the network outputs, which may bring additional benefits when used for other HS image-based applications. The code will be publicly available at https://github.com/jinnh/PDE-Net.

Multi-Task Interaction Learning for Spatiospectral Image Super-Resolution

High spatial resolution and high spectral resolution images (HR-HSIs) are widely applied in geosciences, medical diagnosis, and beyond. However, how to get images with both high spatial resolution and high spectral resolution is still a problem to be solved. In this paper, we present a deep spatial-spectral feature interaction network (SSFIN) for reconstructing an HR-HSI from a low-resolution multispectral image (LR-MSI), e.g., RGB image. In particular, we introduce two auxiliary tasks, i.e., spatial super-resolution (SR) and spectral SR to help the network recover the HR-HSI better. Since higher spatial resolution can provide more detailed information about image texture and structure, and richer spectrum can provide more attribute information, we propose a spatial-spectral feature interaction block (SSFIB) to make the spatial SR task and the spectral SR task benefit each other. Therefore, we can make full use of the rich spatial and spectral information extracted from the spatial SR task and spectral SR task, respectively. Moreover, we use a weight decay strategy (for the spatial and spectral SR tasks) to train the SSFIN, so that the model can gradually shift attention from the auxiliary tasks to the primary task. Both quantitative and visual results on three widely used HSI datasets demonstrate that the proposed method achieves a considerable gain compared to other state-of-the-art methods. Source code is available at https://github.com/junjun-jiang/SSFIN.