Hyperspectral image reconstruction method based on double-ghost attention mechanism network

A hyperspectral image and attention technology, applied in image data processing, neural learning methods, 2D image generation, etc., can solve the problem of complex model calculation and large memory usage

Active Publication Date: 2021-05-18
UNIV OF SHANGHAI FOR SCI & TECH
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  • Application Information

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Problems solved by technology

However, the accuracy of the reconstructed hyperspectral image needs to be further improved. At the same time, the trained model ...

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  • Hyperspectral image reconstruction method based on double-ghost attention mechanism network
  • Hyperspectral image reconstruction method based on double-ghost attention mechanism network
  • Hyperspectral image reconstruction method based on double-ghost attention mechanism network

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Embodiment

[0030] Such as figure 1 As shown, the present embodiment provides a hyperspectral image reconstruction method based on a double-ghost attention mechanism network, which is used to reconstruct a hyperspectral image for a single RGB image, including the following steps:

[0031] Step 1, input an RGB image, and use a 3×3 size convolution kernel to extract it to obtain shallow feature information.

[0032] Step 2, enter the shallow feature information into the first dual-ghost residual attention module to obtain deep-level feature information, specifically: step 2-1, enter the shallow feature information into the first dual-ghost residual The first ghost residual module in the attention module captures the original feature information; step 2-2, enter the original feature information into the second ghost residual module in the first dual ghost residual attention module to obtain deep features information.

[0033] Such as figure 1 with figure 2 As shown, the Dual Ghostnet Re...

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Abstract

The invention provides a hyperspectral image reconstruction method based on a double-ghost attention mechanism network, which is used for performing hyperspectral image reconstruction on a single RGB image, and comprises the following steps: step 1, inputting and processing an RGB image to obtain shallow feature information; 2, enabling the shallow feature information to enter a first double-ghost residual attention module, and obtaining deep feature information; 3, enabling the deep-level feature information to enter a dual-output feature convolution attention mechanism module to extract deeper-level feature information; 4, adding the deeper feature information and the shallow feature information to obtain new features; 5, putting the new features sequentially into subsequent m-1 series double ghost residual attention modules, and finally outputing one feature; 6, performing convolution on the features output in the step 5, and activating to obtain new features; and 7, enabling the feature map with the new features to enter an optimal non-local module, and outputting a hyperspectral image for visualization.

Description

technical field [0001] The invention relates to an image reproduction method, in particular to a hyperspectral image reconstruction method based on a double-ghost attention mechanism network. Background technique [0002] Hyperspectral imaging technology is based on a large number of narrow-band image data technologies. It combines imaging technology with spectral technology to detect the two-dimensional geometric space and spectral information of the target, and obtain high-resolution continuous, narrow-band image data. Hyperspectral images combine image information and spectral information of samples. Image information can reflect the external quality characteristics of the sample such as size, shape, defect, etc. Since different components have different spectral absorption, the image will reflect a certain defect more significantly at a specific wavelength, and the spectral information can fully reflect the quality of the sample. The difference in internal physical stru...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/08G06N3/04
CPCG06T11/00G06N3/08G06N3/045
Inventor 王江薇王文举
Owner UNIV OF SHANGHAI FOR SCI & TECH
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