Patent ID: 11954822
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A constructing method of a combined neural network model, comprising:
obtaining a plurality of trained neural network models, wherein the plurality of neural network models are configured to execute an identical image processing task, input images of the plurality of neural network models are provided with identical resolution, output images of the plurality of neural network models are provided with identical resolution, and any two of the plurality of neural network models are different in at least one of a structure or a parameter;
obtaining outputs of the plurality of neural network models based on an identical verification set, determining evaluation quality of the plurality of neural network models according to a predetermined image quality evaluation criterion, and sorting the plurality of neural network models according to the evaluation quality from high to low;
taking a neural network model with highest evaluation quality as a first neural network model in the combined neural network model; and
determining whether a neural network model with highest evaluation quality among remaining neural network models can be added into a current combined neural network model, if yes, adding the neural network model with the highest evaluation quality among the remaining neural network models into the current combined neural network model, and if not, taking the current combined neural network model as an obtained combined neural network model,
wherein the plurality of neural network models comprise a first neural network model, the first neural network model is configured to perform a first image processing method, and the first image processing method comprises:
obtaining an input image;
obtaining, based on the input image, initial feature images of N stages with resolutions from high to low, wherein N is a positive integer and N>2;
performing, based on initial feature images of second to N-th stages, cyclic scaling processing on an initial feature image of a first stage among the initial feature images of the N stages, to obtain an intermediate feature image; and
performing merging processing on the intermediate feature image to obtain an output image,
wherein the cyclic scaling processing comprises hierarchically-nested scaling processing of N−1 stages, and scaling processing of each stage comprises down-sampling processing, concatenating processing, up-sampling processing, and residual link addition processing;
down-sampling processing of an i-th stage performs, based on an input of scaling processing of the i-th stage, down-sampling to obtain a down-sampling output of the i-th stage,
concatenating processing of the i-th stage performs, based on the down-sampling output of the i-th stage and an initial feature image of an (i+1)-th stage, concatenating to obtain a concatenating output of the i-th stage,
up-sampling processing of the i-th stage obtains an up-sampling output of the i-th stage based on the concatenating output of the i-th stage, and
residual link addition processing of the i-th stage performs residual link addition between the input of the scaling processing of the i-th stage and the up-sampling output of the i-th stage, to obtain an output of the scaling processing of the i-th stage, wherein i=1, 2, ..., N−1; and
scaling processing of a (j+1)-th stage is nested between down-sampling processing of a j-th stage and concatenating processing of the j-th stage, and an output of the down-sampling processing of the j-th stage serves as an input of the scaling processing of the (j+1)-th stage, wherein j=1, 2, ..., N−2,
wherein the obtaining, based on the input image, the initial feature images of the N stages with resolutions from high to low, comprises:
concatenating the input image with a random noise image to obtain a concatenating input image; and
performing analysis processing of N different stages on the concatenating input image, to obtain the initial feature images of the N stages with resolutions from high to low, respectively.