Patent ID: 11875488
Assignee: HENAN UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A method, comprising:
1) optimizing an objective function with a chaotic supply-demand algorithm to enhance a real retinal image to obtain an enhanced real retinal image;
2) synthesizing a virtual retinal image by a hybrid image generation method;
3) stablishing a parallel multi-layer decomposed interval type-2 intuitionistic fuzzy convolutional neural network model based on the virtual retinal image and the enhanced real retinal image; and
4) integrating outputs from a plurality of parallel multi-layer decomposed interval type-2 intuitionistic fuzzy convolutional neural network models as a final classification result;
wherein
1) is implemented as follows:
1.1) performing function transformation M on the real retinal image to obtain an enhanced output image:

EI(i,j)=M(I(i,j)|θ)  (1)

where I(i, j) denotes a part of the real retinal image, and EI(i, j) denotes a corresponding part of the enhanced retinal image, i, j=1, 2, . . . , n; θ=(r, β, ρ, σ) is an undetermined parameter of the function (1);
M(I(i, j)|θ)=M(I(i, j)|r, β, ρ, σ)=rP/(s(i, j)+β)(I(i, j)−ρ×P(i, j))+P(i, j)σ denotes an image enhancement function, P(i, j)=Σp=1nΣq=1nP(p, q)/n2 denotes the average of local pixels of the real retinal image, s(i, j)=(Σp=1nΣq=1n(P(p, q)−P(i, j))2)1/2/n denotes a mean square error of local pixels of the real retinal image, P(p, q) denotes a pixel, n denotes a number of local pixels of the real retinal image, and P denotes a mean of all pixels;
1.2) setting a respective objective function J(θ) according to requirements of image enhancement, and obtaining parameter values when the objective function is optimized by the chaotic supply-demand algorithm, thus obtaining a reasonable image enhancement transformation function, and obtaining the enhanced real retinal image:

J(θ)=npe(EI(θ))×log(log(EI(θ)))×entropy(EI(θ))  (2)

where J(θ) denotes the objective function, npe(EI(θ)) denotes the number of pixels at an edge of the enhanced output image, log(log(EI(θ))) denotes the Sobel operator for a density at the edge of the enhanced output image, entropy(EI(θ))=−Σt=1256pt log2pt, where pt denotes a probability of the tth brightness level of the enhanced image;
the chaotic supply-demand algorithm comprises:
1.2.1) initializing market populations:
given the number of populations is Np and the dimensionality of a product price vector pi and of a production quantity vector qi is dim(θ)=4, determining a value range for pi and qi according to a search range for parameters in each dimension in a parameter vector θ=(r, β, ρ, σ), pij=[pj,pj], qij=[qj,qj];
selecting, for pij and qij, uniformly distributed random numbers in a unit interval respectively to obtain an initial unit product price population pi(0) and quantity population qi(0);
iteratively calculating a two-dimensional Ikeda map by respectively using pij(0) and qij(0) as initial values, to obtain a candidate initial product price population pi and quantity population qi by chaos iteration, 1≤i≤NpNT, where NT denotes a number of iterations;
for pij and qij in each dimension, performing linear transformation to obtain a candidate initial product price {tilde over (p)}i and quantity {tilde over (q)}i; and
calculating, according to Formula (2), an objective function value J({tilde over (p)}i) and J({tilde over (q)}i) respectively for the price and quantity of each product in a population and then normalizing, respectively comparing the objective function values J({tilde over (p)}i) and J({tilde over (q)}i) with the uniformly distributed random numbers, and selecting Np product prices and quantities to form an initial production price population and an initial quantity population;
1.2.2) determining the value of parameters and calculating an initial optimal product:
comparing the objective function values of the price and quantity of a product in the initial production price population and in the initial quantity population, respectively, and replacing {tilde over (p)}i with {tilde over (q)}i if J({tilde over (p)}i)<J({tilde over (q)}i), where the product price with the greatest objective function value is {tilde over (p)}best=argmax1≤i≤NpJ({tilde over (p)}i);
1.2.3) performing the following iteration process when the number of iterations t≤Ter, where Ter denotes a maximum number of iterations:
calculating an absolute variance of the objective function for the quantity of a product in a population, normalizing, and selecting the product quantity {tilde over (q)}k as an equilibrium quantity point {tilde over (q)}e according to an roulette method; while for the determination of an equilibrium point for the price of a product, comprising two stages: in the first stage, at t≤Ter/2, calculating the absolute variance of the objective function for the price of a product in a population, normalizing, and selecting the product price {tilde over (p)}l as an equilibrium point {tilde over (p)}e according to the roulette method, and in the second stage, at t>Ter/2, using the average of the product price in the population as the equilibrium point for the price of a product;
comparing the objective function values, replacing {tilde over (p)}i(t+1) with {tilde over (q)}i(t+1) if J({tilde over (p)}i(t+1))<J({tilde over (q)}i)(t+1)), {tilde over (p)}b(t+1)=argmax1≤m≤NpJ({tilde over (p)}m(t+1)), if J({tilde over (p)}b(t+1))>J({tilde over (p)}best), then {tilde over (p)}best={tilde over (p)}b(t+1), otherwise keeping {tilde over (p)}best unchanged; and disturbing by logistic chaotic mapping if {tilde over (p)}best remains unchanged after ten successive iterations and Ter has not yet been reached;
1.2.4) at the end of iteration, outputting an optimal solution {tilde over (p)}best, where an enhanced image EI=M(I|{tilde over (p)}best) is the enhanced real retinal image.