Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution

Zhang, Lei; Nie, Jiangtao; Wei, Wei; Zhang, Yanning; Liao, Shengcai; Shao, Ling

doi:10.1109/cvpr42600.2020.00314

Cited by 110 publications

(47 citation statements)

References 29 publications

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“…Traditional methods have achieved favorable performances by exploiting the priors (e.g., sparsity and low-rankness), but such priors may not hold in some complicated scenarios [9,17,18].…”

Section: Traditional Methodsmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Chen

Zheng

2021

Remote Sensing

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Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

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“…Traditional methods have achieved favorable performances by exploiting the priors (e.g., sparsity and low-rankness), but such priors may not hold in some complicated scenarios [9,17,18].…”

Section: Traditional Methodsmentioning

confidence: 99%

“…In general, previous methods mainly focus on exploiting various handcrafted priors (e.g., sparsity and low-rankness) to improve the quality of the reconstructed HR HSI [9]. However, sparsity and low-rankness priors may not hold in real complicated scenarios [17], which can result in unsatisfactory super-resolved results [18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Chen

Zheng

2021

Remote Sensing

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“…However, the DHPs can only leverage the observed LR-HS image for network training and cannot efficiently learn both the spatial structure and spectral attribute image priors for reconstructing a latent HR-HS image. Furthermore, Zhang et al [ 42 ] leveraged the generated training triplets (the LR-HS, HR-RGB, and HR-HS images) with different degradation models to learn a common deep model for predicting an initial HR-HS image, and then exploited unsupervised adaptation learning for fine-turning the initial estimation and automatically learning the degradation operations of the under-studying observations. Although remarkable performance gain has been achieved with different degradation models compared with most state-of-the-art methods, the performance of the fine-turning HR-HS image in the adaptation learning is greatly affected by the initially estimation in the common model, and is easy to fall into a local minimum solution.…”

Section: Related Research Workmentioning

confidence: 99%

Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution

Liu

Zheng

Han

2021

Sensors

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Hyperspectral image (HSI) super-resolution (SR) is a challenging task due to its ill-posed nature, and has attracted extensive attention by the research community. Previous methods concentrated on leveraging various hand-crafted image priors of a latent high-resolution hyperspectral (HR-HS) image to regularize the degradation model of the observed low-resolution hyperspectral (LR-HS) and HR-RGB images. Different optimization strategies for searching a plausible solution, which usually leads to a limited reconstruction performance, were also exploited. Recently, deep-learning-based methods evolved for automatically learning the abundant image priors in a latent HR-HS image. These methods have made great progress for HS image super resolution. Current deep-learning methods have faced difficulties in designing more complicated and deeper neural network architectures for boosting the performance. They also require large-scale training triplets, such as the LR-HS, HR-RGB, and their corresponding HR-HS images for neural network training. These training triplets significantly limit their applicability to real scenarios. In this work, a deep unsupervised fusion-learning framework for generating a latent HR-HS image using only the observed LR-HS and HR-RGB images without previous preparation of any other training triplets is proposed. Based on the fact that a convolutional neural network architecture is capable of capturing a large number of low-level statistics (priors) of images, the automatic learning of underlying priors of spatial structures and spectral attributes in a latent HR-HS image using only its corresponding degraded observations is promoted. Specifically, the parameter space of a generative neural network used for learning the required HR-HS image to minimize the reconstruction errors of the observations using mathematical relations between data is investigated. Moreover, special convolutional layers for approximating the degradation operations between observations and the latent HR-HS image are specifically to construct an end-to-end unsupervised learning framework for HS image super-resolution. Experiments on two benchmark HS datasets, including the CAVE and Harvard, demonstrate that the proposed method can is capable of producing very promising results, even under a large upscaling factor. Furthermore, it can outperform other unsupervised state-of-the-art methods by a large margin, and manifests its superiority and efficiency.

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“…Deng proposed an algorithm, named SR by Neural Texture Transfer (SRNTT) [39], which implemented SR in a referential way. This year, a large number of SR methods for specific objects have emerged, such as hyperspectral SISR [40], face SR [41], and so on.…”

Section: Single Image Super-resolution (Sisr)mentioning

confidence: 99%

Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

Wang

Teng

2021

Electronics

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With the help of deep neural networks, video super-resolution (VSR) has made a huge breakthrough. However, these deep learning-based methods are rarely used in specific situations. In addition, training sets may not be suitable because many methods only assume that under ideal circumstances, low-resolution (LR) datasets are downgraded from high-resolution (HR) datasets in a fixed manner. In this paper, we proposed a model based on Generative Adversarial Network (GAN) and edge enhancement to perform super-resolution (SR) reconstruction for LR and blur videos, such as closed-circuit television (CCTV). The adversarial loss allows discriminators to be trained to distinguish between SR frames and ground truth (GT) frames, which is helpful to produce realistic and highly detailed results. The edge enhancement function uses the Laplacian edge module to perform edge enhancement on the intermediate result, which helps further improve the final results. In addition, we add the perceptual loss to the loss function to obtain a higher visual experience. At the same time, we also tried training network on different datasets. A large number of experiments show that our method has advantages in the Vid4 dataset and other LR videos.

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Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution

Cited by 110 publications

References 29 publications

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution

Video Super-Resolution Based on Generative Adversarial Network and Edge Enhancement

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