A speckle-based approach to compressive hyperspectral imaging
A speckle-based approach to compressive hyperspectral imaging
 
  Incorporating wavelength information into a monochrome image is of great interest for imaging spectroscopy. Here, we show that by exploiting the properties of multiple scattering materials, we can encode spectral information in a CMOS image, where the spectral resolution obtained is only limited by the scattering strength of the material. As a proof-of-concept, we demonstrate this technique using a thin multiple scattering layer of gallium phosphide nanowires and a microlens array. We achieve a spectral resolution of approximately 4 nm and a reconstructed image containing 64 pixels. We demonstrate that a computational technique which is commonly used in compressive sensing can be used to reconstruct both sparse and dense spectra, when undersampling and oversampling a signal, respectively. This method provides an ultra-compact solution to obtaining both spatial and spectral information in one measurement, for potential use in portable spectroscopy.
compressive sensing, hyperspectral imaging, multiple scattering, speckle
  
  1-8
  
  
    
      French, Rebecca
      
        d6d6a85a-e351-4cc8-ae4a-827c35fe6b64
      
     
  
    
      Gigan, Sylvain
      
        f2f2026a-fbe0-4fbe-b3a1-d8f7b35cdd58
      
     
  
    
      Muskens, Otto L.
      
        2284101a-f9ef-4d79-8951-a6cda5bfc7f9
      
     
  
  
   
  
  
    
    
  
  
    
      French, Rebecca
      
        d6d6a85a-e351-4cc8-ae4a-827c35fe6b64
      
     
  
    
      Gigan, Sylvain
      
        f2f2026a-fbe0-4fbe-b3a1-d8f7b35cdd58
      
     
  
    
      Muskens, Otto L.
      
        2284101a-f9ef-4d79-8951-a6cda5bfc7f9
      
     
  
       
    
 
  
    
      
  
  
  
  
    French, Rebecca, Gigan, Sylvain and Muskens, Otto L.
  
  
  
  
   
    (2018)
  
  
    
    A speckle-based approach to compressive hyperspectral imaging.
  
  
  
  
   In Next-Generation Spectroscopic Technologies XI. 
  vol. 10657, 
      SPIE. 
          
          
        .
    
  
  
  
   (doi:10.1117/12.2303993).
  
   
  
    
      Record type:
      Conference or Workshop Item
      (Paper)
      
      
    
   
    
      
        
          Abstract
          Incorporating wavelength information into a monochrome image is of great interest for imaging spectroscopy. Here, we show that by exploiting the properties of multiple scattering materials, we can encode spectral information in a CMOS image, where the spectral resolution obtained is only limited by the scattering strength of the material. As a proof-of-concept, we demonstrate this technique using a thin multiple scattering layer of gallium phosphide nanowires and a microlens array. We achieve a spectral resolution of approximately 4 nm and a reconstructed image containing 64 pixels. We demonstrate that a computational technique which is commonly used in compressive sensing can be used to reconstruct both sparse and dense spectra, when undersampling and oversampling a signal, respectively. This method provides an ultra-compact solution to obtaining both spatial and spectral information in one measurement, for potential use in portable spectroscopy.
        
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  More information
  
    
      e-pub ahead of print date: 14 May 2018
 
    
  
  
    
  
    
  
    
     
        Venue - Dates:
        Next-Generation Spectroscopic Technologies XI 2018, , Orlando, United States, 2018-04-16 - 2018-04-18
      
    
  
    
  
    
  
    
     
        Keywords:
        compressive sensing, hyperspectral imaging, multiple scattering, speckle
      
    
  
    
  
    
  
  
        Identifiers
        Local EPrints ID: 422926
        URI: http://eprints.soton.ac.uk/id/eprint/422926
        
          
        
        
        
        
          PURE UUID: 607207a5-2dbd-419b-be9a-e871008ae8c5
        
  
    
        
          
            
          
        
    
        
          
        
    
        
          
            
              
            
          
        
    
  
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  Date deposited: 08 Aug 2018 16:30
  Last modified: 16 Mar 2024 04:01
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      Contributors
      
          
          Author:
          
            
              
              
                Rebecca French
              
              
            
            
          
        
      
          
          Author:
          
            
            
              Sylvain Gigan
            
          
        
      
        
      
      
      
    
  
   
  
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