| CPC G06V 10/82 (2022.01) [G06V 10/58 (2022.01); G06V 10/751 (2022.01)] | 20 Claims |
|
1. A system for hyperspectral image generation, comprising:
a computing device comprising at least a memory and a processor;
a spectral band grouping module comprising a first plurality of programming instructions that, when operating on the processor, cause the computing device to:
obtain a training hyperspectral image;
identify a plurality of spectral bands in the training hyperspectral image;
compute a correlation coefficient of each spectral band of the plurality of spectral bands to at least one other spectral band of the plurality of spectral bands; and
form a plurality of spectral domain groups based on the computed correlation coefficients;
a decomposition module comprising a second plurality of programming instructions that, when operating on the processor, cause the computing device to:
obtain the plurality of spectral domain groups from the spectral band grouping module;
obtain an RGB (red-green-blue) input image;
provide the RGB input image and plurality of spectral domain groups to a first neural network, wherein the first neural network includes at least one convolutional block, and at least one residual block; and
obtain as an output of the first neural network, a reconstructed hyperspectral image, based on the RGB input image; and
a fine-tuning module comprising a third plurality of programming instructions that, when operating on the processor, cause the computing device to:
provide the reconstructed hyperspectral image to a second neural network, wherein the second neural network includes at least one convolutional block, and at least one residual block;
obtain as an output of the second neural network, a reconstructed RGB image;
compare the reconstructed RGB image to the RGB input image by computing a spectral similarity metric between the reconstructed RGB image and the RGB input image, wherein the spectral similarity metric is based on correlation coefficients between corresponding spectral bands of the images; and
adjust one or more weights of the first neural network based on the computed spectral similarity metric to minimize spectral distortion between the reconstructed RGB image to the RGB input image.
|