Hyperspectral Image Super-Resolution Reconstruction Method Based on Spectral Space Combination and Gradient Domain Loss

A hyperspectral image, gradient domain technology, applied in the field of hyperspectral image super-resolution reconstruction, can solve the problems of inability to capture hyperspectral images, inability to balance time complexity and reconstruction quality, poor reconstruction quality, etc. Reconstruction effect, improvement of reconstruction effect, effect of accelerating convergence speed

Active Publication Date: 2021-02-02
CHONGQING UNIV
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0005] 1) The time complexity and reconstruction quality of the method cannot be balanced;
[0006] 2) Existing methods cannot capture the spectral information of hyperspectral images to combine spectrum and space, and the reconstruction quality is poor

Method used

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  • Hyperspectral Image Super-Resolution Reconstruction Method Based on Spectral Space Combination and Gradient Domain Loss
  • Hyperspectral Image Super-Resolution Reconstruction Method Based on Spectral Space Combination and Gradient Domain Loss
  • Hyperspectral Image Super-Resolution Reconstruction Method Based on Spectral Space Combination and Gradient Domain Loss

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Experimental program
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Embodiment

[0083]1. Experimental data

[0084]The CAVE data set contains 32 hyperspectral data from different objects. Each object has 31 photos illuminated by different wavelengths. Each image is 512x512 in size and the wavelength range is 400nm-700nm (each wavelength interval is 10nm). The Harvard data set contains 50 real-world outdoor / indoor data under sunlight and 27 data under artificial synthetic light. The size of each data block is 1392x1040x31, and these 31 bands are evenly distributed between 420nm-720nm. For each Harvard sample, this experiment will cut it into a size of 1024x1024x31 to facilitate calculation.

[0085]In order to facilitate the comparison of the quality of reconstructed images, this experiment uses the average value of the wavelength range of 400nm-500nm (or 420nm-520nm) as the B channel of the color image, and the average value of 500nm-600nm (or 520nm-620nm) as the color image G channels, the average value of 600nm-700nm (or 620nm-720nm) is used as the R channel of the...

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Abstract

The invention relates to a hyperspectral image super-resolution reconstruction method based on spectrum-space combination and gradient domain loss. The method includes steps: S1, obtaining a hyperspectral image; S2, dividing the hyperspectral image into a training set and a test set; S3, inputting the training set to a neural network with spectrum-space combination, and performing training by employing a combined loss of a spatial domain and a gradient domain; and S4, enabling the test set to pass through the neural network, and obtaining a final reconstruction result. According to the adoptedmethod, compared with the prior art, light weight is achieved in a network structure, the reconstruction quality is higher, and the anti-noise performance is great.

Description

Technical field[0001]The invention belongs to the field of image super-resolution reconstruction, and relates to a hyperspectral image super-resolution reconstruction method based on spectral space combined network and gradient domain loss.Background technique[0002]The low spatial resolution of hyperspectral images makes it easy to produce mixed end members, resulting in spectral distortion and destroying the spatial and spectral consistency of end members. In terms of input information, super-resolution reconstruction of hyperspectral images can be roughly divided into two types: reconstruction methods based on image fusion (with auxiliary images) and reconstruction methods without auxiliary images. The reconstruction method based on image fusion uses RGB images or panchromatic (PAN) images or multispectral (MS) images as an aid, and uses spatial and spectral information to jointly constrain, thereby unmixing endmembers and reducing spectral distortion. Using the RGB-assisted recon...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 王敏全丁溢洋尚赵伟秦安勇赵林畅
Owner CHONGQING UNIV
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