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                            Library & Multimedia
 Model-Guided Deep Hyperspectral Image Super-Resolution
NCJ Number
              304092

Author(s)
          Weisheng Dong;
                        et al
Date Published

2021
Length
              15 pages
Annotation
              This article proposes an iterative Hyperspectral Image Super-Resolution (HSISR) algorithm based on a deep HSI denoiser to leverage both domain knowledge likelihood and deep image prior.
Abstract
              The trade-off between spatial and spectral resolution is one of the fundamental issues in hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution hyperspectral images (HR-HSI), a compromised solution is to fuse a pair of images: one has high-resolution (HR) in the spatial domain but low-resolution (LR) in spectral-domain and the other vice versa. Model-based image fusion methods including pan-sharpening aim at reconstructing HR-HSI by solving manually designed objective functions; however, such hand-crafted prior often leads to inevitable performance degradation due to a lack of end-to-end optimization. Although several deep learning-based methods have been proposed for hyperspectral pan-sharpening, HR-HSI related domain knowledge has not been fully exploited, leaving room for further improvement. By taking the observation matrix of HSI into account during the end-to-end optimization, the current study showed how to unfold an iterative HSISR algorithm into a novel model-guided deep convolutional network (MoG-DCN). The representation of the observation matrix by sub-networks also enables the unfolded deep HSISR network to work with different HSI situations, which enhances the flexibility of MoG-DCN. Extensive experimental results are reported to demonstrate that the proposed MoG-DCN outperforms several leading HSISR methods in terms of both implementation cost and visual quality. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/MoG-DCN.htm. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2018-75-CX-0032


  
  
        
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  Award
        Using Artificial Intelligence Technologies to Expose Darknet Opioid Traffickers
    
  
  
Date Published: January 1, 2021
    
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