Robust Deep Hyperspectral Imagery Super-Resolution

Nie, Jiangtao; Zhang, Lei; Wei, Wei; Zhang, Yanning

doi:10.1109/igarss.2019.8900117

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…HSI Image Super-resolution [99] Used UNNP as the prior of the latent HR HSI -CAVE and Washington DC datasets -RMSE, PSNR, SAM & SSIM Provided superior performance as compared to hand-crafted approaches on two benchmark hyperspectral imaging datasets.…”

Section: Compressed Sensingmentioning

confidence: 99%

“…This is too idealistic for real cases. To address the problem of noisy HSI and MSI input, Nie et al [99] proposed a UNNP-based HSI super-resolution method in which UNNP is employed as the prior of the latent HR HSI which converts it into an end-to-end DL problem and solved it using back-propagation to capture better statistics of the latent HR HSI. Furthermore, they demonstrated the effectiveness of their proposed technique by evaluating it on two benchmark datasets: the CAVE dataset and the Washington DC dataset.…”

Section: Hyperspectral Image Super Resolutionmentioning

confidence: 99%

See 1 more Smart Citation

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem. Recently, a new paradigm of training deep models using a single image, named untrained neural network prior (UNNP) has been proposed to solve a variety of inverse tasks, e.g., restoration and inpainting. Since then, many researchers have proposed various applications and variants of UNNP. In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research.

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Section: Compressed Sensingmentioning

confidence: 99%

Section: Hyperspectral Image Super Resolutionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…The results generated by using existing techniques are based on the assumption that both of the input images are clean which is too idealistic for real cases. To address the problem of noisy HSI and MSI input, Nie et al [61] proposed a UNNP-based HSI super-resolution method. UNNP is employed as the prior of the latent HR HSI which converts it into an end-to-end DL problem.…”

Section: Hyperspectral Image Super Resolutionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum¹,

Ilahi²,

Shamshad³

et al. 2021

Preprint

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“…We analyze the reason as follows. For DIP based SR method [15,30], it utilizes the random noise for initialization, and then gradually approximates the optimum solution (i.e. the final reconstructed image).…”

Section: Efficient Deep Hsi Reconstruction Network With Deep Image Priormentioning

confidence: 99%

“…It is noticeable that the spectral and spatial degeneration models we utilized to generate the LR HSI and HR MSI are different with those utilized in test. Specifically, the bluer kernel k is initialed with the Gaussian distribution, which kernel size and standard deviation are randomly chosen within the ranges [5,15] and [0.5, 2], respectively. In addition, c used for the spectral response matrix (i.e., spectral degeneration model) is chosen within the range [5e-3, 8e-3].…”

Section: Training Details Of Backbone Networkmentioning

confidence: 99%

Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution

Nie,

Zhang,

Wei

et al. 2020

Preprint

Self Cite

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Despite the great success of deep model on Hyperspectral imagery (HSI) super-resolution(SR) for simulated data, most of them function unsatisfactory when applied to the real data, especially for unsupervised HSI SR methods. One of the main reason comes from the fact that the predefined degeneration models (e.g. blur in spatial domain) utilized by most HSI SR methods often exist great discrepancy with the real one, which results in these deep models overfit and ultimately degrade their performance on real data. To well mitigate such a problem, we explore the unsupervised blind HSI SR method. Specifically, we investigate how to effectively obtain the degeneration models in spatial and spectral domain, respectively, and makes them can well compatible with the fusion based SR reconstruction model. To this end, we first propose an alternating optimization based deep framework to estimate the degeneration models and reconstruct the latent image, with which the degeneration models estimation and HSI reconstruction can mutually promotes each other. Then, a meta-learning based mechanism is further proposed to pre-train the network, which can effectively improve the speed and generalization ability adapting to different complex degeneration. Experiments on three benchmark HSI SR datasets report an excellent superiority of the proposed method on handling blind HSI fusion problem over other competing methods.

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Robust Deep Hyperspectral Imagery Super-Resolution

Cited by 7 publications

References 8 publications

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution

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