Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

Dong, Weisheng; Fu, Fazuo; Shi, Guangming; Cao, Xun; Wu, Jinjian; Li, Guangyu; Li, Xin

doi:10.1109/tip.2016.2542360

Cited by 453 publications

(302 citation statements)

References 41 publications

Supporting

Mentioning

278

Contrasting

Unclassified

Order By: Relevance

“…Since then, this classifier has been applied in numerous fields such as pattern recognition [14, 24], object detection [25], image restoration [26], image denoising [27], video restoration [28], image super-resolution [29]. For the following, D represents a dataset; s donates a sample; X , Y , or Z stands for a coefficient; and α or β is a positive scalar.…”

Section: Representation Based Classifiersmentioning

confidence: 99%

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Shu

Zhang

Tang

2017

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

show abstract

Section: Representation Based Classifiersmentioning

confidence: 99%

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Shu

Zhang

Tang

2017

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…Eismann and Hardie (2004), Eismann and Hardie (2005), Chen et al (2014) and Zhao et al (2011) used a high resolution panchromatic image for HSIs. Ma et al (2013) and Dong et al (2016) employed an RGB video/image with high spatial resolution to enhance a single HSI with low spatial resolution.…”

Section: Hyperspectral Image Super-resolutionmentioning

confidence: 99%

Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

Lam

Sato

et al. 2016

Int J Comput Vis

View full text Add to dashboard Cite

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often suffer from degradation such as noise and low resolution. In this paper, we propose an effective model for hyperspectral image (HSI) restoration, specifically image denoising and super-resolution. Our model considers three underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the degraded HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI restoration model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. In experiments on HSI denoising, we show that the proposed method outperforms many state-of-the-art methods under several comprehensive quantitative assessments. We also show that our method performs well on HSI super-resolution.

show abstract

“…The k-nearest neighbor method given by [12] is very simple. It find out the closest pixel value to the specify input pixel.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Image Resolution: A Survey

Mittal¹,

Singh²

2016

CAE

View full text Add to dashboard Cite

The High Resolution images have great importance in various fields, such as astronomy, medical imaging, agricluture, video surveillance, etc. These High Resolution images are useful to get the required details which are important for analysis in many applications. This paper investigates mainly on various modern existing methods of super resolution that and putting it all together for a literature survey. Scope of this study mainly focuses on the different available techniques of image processing to get high resolution images for extracting the meticulous details.

show abstract

Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

Cited by 453 publications

References 41 publications

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

Enhancement of Image Resolution: A Survey

Contact Info

Product

Resources

About