Hyperspectral Image Superresolution Based on Double Regularization Unmixing

Zou, Changzhong; Xia, Youshen

doi:10.1109/lgrs.2017.2692958

Cited by 8 publications

(11 citation statements)

References 21 publications

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“…The work [6] is focused on preserving spatial structural information like textures, edges in SR image reconstruction and proposed a new HSI super resolution algorithm based on double regularization un-mixing technique. Basically it has used both hyper spectral image (HSI) as well as multi spectral images (MSI).…”

Section: Super Resolution Of Hyper Spectral Images (Hsi)mentioning

confidence: 99%

“…It reduces contrast distortion. SR process in [6] initially generated LR hyper spectral image by blurring reference image by using a blur kernel, then down sampled so generated image with sampling ratio of 4. Exponential & Blur kernels of 5×5 dimensions are used.…”

Section: Super Resolution Of Hyper Spectral Images (Hsi)mentioning

confidence: 99%

“…In more simpler terms, many super resolution techniques have evolved as & when a need for it arisen, viz. in area of face recognition [2] with surveillance applications [2], [3], [4], satellite imaging [5], [6] with an objective of recognizing special image parts like watery bodies, forest areas which is required in military applications, Diagnosis of diseases in medical science [7], [8]. In short, depending on application areas the usability of a particular algorithm or technique changes.…”

Section: Introductionmentioning

confidence: 99%

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A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

Borse*¹,

Patil²

2019

IJRTE

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Now-a-days many applications dealing with visual content need to access underlying details in the image or video of interest. For instance, detailing is required to take life critical decisions for further action plans by a doctor. Clarity and structural information are some of the aspects of detailing. It can be achieved by cost effective software solution like super resolution reconstruction of an image. Super resolution (SR) deals in increasing resolution of an image to make it more clear and valid for use. Many SR techniques exist with variable goals to achieve. With this intension a new technique for preserving structural information in the reconstruction process is proposed. The system extends a deep convolution neural network by adding a new optimization layer at the end of network activation layer. This new layer maintains permissible error threshold in the acquired signal and tries to improve the signal by feeding back latest reconstructed frame. The proposed system shows noticeable improvement in structural similarity of reconstructed images as compared with the ground truth.

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Section: Super Resolution Of Hyper Spectral Images (Hsi)mentioning

confidence: 99%

Section: Super Resolution Of Hyper Spectral Images (Hsi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

Borse*¹,

Patil²

2019

IJRTE

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“…That work has then been generalized [18], to be more robust with respect to the blur kernel, and also slightly more efficient. Recently, Zou and Xia [19] use an instance of non-negative factorization to compute endmembers and abundances with graph Laplacian regularization, without however making use of the sum-to-one constraint.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

2017

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Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

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“…Simoes et al [26] proposed a convex subspace-based formulation by considering a total variation abundance regularization. Zou et al [29] proposed a double regularization HSI super-resolution by introducing the spatial structure information and the nonnegative factorization. Veganzones et al [27] proposed a local dictionary learning to exploit locally low rank property for HSI super-resolution.…”

Section: Introductionmentioning

confidence: 99%

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

Gao

2018

Remote Sensing

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Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

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Hyperspectral Image Superresolution Based on Double Regularization Unmixing

Cited by 8 publications

References 21 publications

A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

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