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Hyperspectral image super-resolution reconstruction method based on spectrum-space combination and gradient domain loss

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

Active Publication Date: 2018-11-16
CHONGQING UNIV
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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 spectrum-space combination and gradient domain loss
  • Hyperspectral image super-resolution reconstruction method based on spectrum-space combination and gradient domain loss
  • Hyperspectral image super-resolution reconstruction method based on spectrum-space combination and gradient domain loss

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Experimental program
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Embodiment

[0083] 1. Experimental data

[0084]The CAVE dataset contains 32 hyperspectral data from different objects, each object has 31 photos under different wavelength bands, each image size is 512x512, and the wavelength range is 400nm-700nm (each band is 10nm apart). The Harvard dataset contains 50 real-world outdoor / indoor data under sunlight and 27 data under artificial light. Each data block size is 1392x1040x31, and the 31 bands are uniformly distributed between 420nm-720nm. For each Harvard sample, this experiment is cropped to a size of 1024x1024x31 for easy calculation.

[0085] In order to compare the quality of the reconstructed images conveniently, in this experiment, the average value of the wavelength band in the range of 400nm-500nm (or 420nm-520nm) is used as the B channel of the color image, and the average value of 500nm-600nm (or 520nm-620nm) is used as the color image. The G channel, the mean value of 600nm-700nm (or 620nm-720nm) was taken 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 spectrum-space combination network and gradient domain loss. Background technique [0002] Due to the low spatial resolution of hyperspectral images, mixed endmembers are easily generated, resulting in spectral distortion and destroying the spatial and spectral consistency of endmembers. Distinguished from the input information, hyperspectral image super-resolution reconstruction 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 image or panchromatic (Panchromatic, PAN) image or multispectral (Multispectral, MS) image as an auxiliary, and uses spatial and spectral information to jointly constrain, thereby unmixing endmembers an...

Claims

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Application Information

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Patent Type & Authority Applications(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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