Patent ID: 11920981
Assignee: BEIJING SEETRUM TECHNOLOGY CO., LTD.
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A spectral restoring method, characterized by comprising:
acquiring a light energy response signal matrix output by a photosensitive chip of a spectral imaging device and a standard spectrum;
determining a primitive restoring function and a response signal vector of the primitive restoring function based on the light energy response signal matrix, wherein the primitive restoring function restores a spectral image value of a predetermined channel corresponding thereto using a predetermined pixel value of the photosensitive chip and pixel values in the vicinity thereof;
acquiring a restoring tensor, wherein the product of the restoring tensor and the response signal vector is equal to an output of the primitive restoring function based on the response signal vector;
obtaining a restored spectral image based on the product of the restoring tensor and the response signal vector; and
outputting the restored spectral image to a display of a client terminal,
wherein the light energy response signal matrix is represented as a matrix B including two dimensions of an image width w and an image height h, and the number of dimensions of the standard spectrum is l, and a distance from the product of a spectral image true value tensor received by the spectral imaging device and the standard spectrum to a spectral image tensor to be restored is set to be minimum,
wherein the standard spectrum is represented as s, and a channel standard spectrum corresponding to a kth channel of the standard spectrum is represented as sk, whereby:

xk→O(i,j)sk 

where xk is a spectral image value of the kth channel of a certain spectral pixel, O(i,j) is a spectral curve true value tensor of a certain spectral pixel, and → represents that a Euclidean distance between tensors is minimum,
wherein a solving process of the restoring tensor comprises:
establishing a first constraint equation based on the restoring tensor, a spectral response tensor block of the spectral response tensor of the spectral imaging device corresponding to the primitive restoring function, and the standard spectrum;
establishing a second constraint equation based on the restoring tensor and the spectral response tensor block; and
obtaining the restoring tensor based on the first constraint equation and the second constraint equation,

wherein the first constraint equation is represented as:

fk({circumflex over (b)},Âsk)=1

where fk is a neural network composed of a connection layer and an activation layer, and Â is a spectral response tensor block A(i−p:i+p,j−p:j+p,:) corresponding to the primitive restoring function in the spectral response tensor, and first and second orders of the tensor block are rearranged to the same order to form a matrix in the shape of (a2,l),
wherein the second constraint equation is represented as:

fk({circumflex over (b)},Â)→0,

wherein obtaining the restoring tensor based on the first constraint equation and the second constraint equation comprises:
obtaining the restoring tensor based on the first constraint equation, the second constraint equation and a third constraint equation, the third constraint equation being represented as:

fk(N*{circumflex over (b)},Âsk)→1

fk(N*{circumflex over (b)},Â)→0

where N is a noise factor and is a random number matrix in the shape of (a,a), which is expected to be 0 and obeys a Gaussian distribution,
wherein the spectral restoring method further comprises establishing a fourth constraint equation:

fk(N*{circumflex over (b)},Ât)→t·sk,

wherein the obtaining the restoring tensor based on the first constraint equation, the second constraint equation and the third constraint equation comprises:
training the neural network based on a data set for spectral restoring by the trained neural network.