【元胞自动机】基于元胞自动机实现艺术图像处理附matlab代码

本文链接：https://blog.youkuaiyun.com/matlab_dingdang/article/details/126356209

1 内容介绍

元胞自动机变换为我们提供了一个将已知现象和元胞自动机演化联系起来的直接方法.它将物理网格空间上的每个点通过元胞自动机变换基映射到元胞自动机空间上,通过元胞自动机空间上的变换系数揭示出物理空间中很难观察到的性质.元胞自动机变换最大的优势是能产生大量的不同性质的基函数,这些基函数可以适应已知问题的各种特性,为图像压缩,数据加密,求解积分方程等方面的应用提供一个良好的平台. 本文先对元胞自动机变换的发展情况和元胞自动机变换的基本方法作了简要的介绍,在此基础上把元胞自动机变换应用到图像处理中。

2 仿真代码

function [ opImgCell ] = CAPainting( Img, mu, lambda, Generation, Boundary )

%CAPAINTING Summary of this function goes here

% Detailed explanation goes here

Img = double(Img);

Sz = size(Img);

Pop = cell(1, mu+lambda);

Result = cell(1, mu+lambda);

if exist('Save', 'dir') ~= 7

mkdir('Save');

end

% initialize first population

for i=1:mu+lambda

if i<= mu

Pop{i} = cell(1, 7);

Pop{i}{1} = randi([1, Boundary{1}], 1, 3); % Birth

Pop{i}{2} = randi([1, Boundary{2}], 1, 3); % Level

Pop{i}{3} = randi([1, Boundary{3}], 1, 3); % number of Pattern Pos

Pop{i}{6} = randi([Boundary{4}, Boundary{5}], 1, 3); % Cell Generation

Pop{i}{4} = cell(1, 3); % PosH

Pop{i}{5} = cell(1, 3); % PosW

Pop{i}{7} = cell(1, 3); % Pattern

for j=1:3

Pop{i}{4}{j} = randi([1, Boundary{6}], 1, Pop{i}{3}(j));

Pop{i}{5}{j} = randi([1, Boundary{7}], 1, Pop{i}{3}(j));

Pop{i}{7}{j} = randi([0, 1],2*Pop{i}{2}(j)+1, 2*Pop{i}{2}(j)+1);

end

Result{i} = zeros(Sz);

for cc=1:3

[Result{i}(:, :, cc), ta, tb, tc, td, te] = GUILogicRule(Img, cc, Pop{i}{1}(cc), Pop{i}{2}(cc), Pop{i}{3}(cc), Pop{i}{6}(cc), 1, Pop{i}{4}{cc}, Pop{i}{5}{cc}, Pop{i}{7}{cc});

end

else

Pop{i} = cell(1, 7);

Result{i} = zeros(Sz);

end

% end initialize

Fit = Fitness(Result);

for g=1:Generation

% tournament size = 2;

SelCounter = 1;

selectMutation = zeros(1, mu);

for sel=1:mu

selectArray = randi([1, mu], 1, 2);

if Fit(selectArray(1)) > Fit(selectArray(2))

selectMutation(SelCounter) = selectArray(1);

SelCounter = SelCounter +1;

else

selectMutation(SelCounter) = selectArray(2);

SelCounter = SelCounter +1;

end

for mutate=1:lambda

[Pop{mu+mutate}, cc] = Mutation(Pop{selectMutation(mutate)}, Boundary);

Result{mu+mutate} = Result{selectMutation(mutate)};

[Result{mu+mutate}(:, :, cc), ta, tb, tc, td, te] = GUILogicRule(Img, cc, Pop{mu+mutate}{1}(cc), Pop{mu+mutate}{2}(cc), Pop{mu+mutate}{3}(cc), Pop{mu+mutate}{6}(cc), 1, Pop{mu+mutate}{4}{cc}, Pop{mu+mutate}{5}{cc}, Pop{mu+mutate}{7}{cc});

end

Fit = Fitness(Result);

% survivor selection

[Fit,sortIndex] = sort(Fit(:),'descend');

Fit = [Fit(1:mu)', zeros(1, lambda)];

selectParent = sortIndex(1:mu);

newPop = cell(1, mu+lambda);

newRes = cell(1, mu+lambda);

for wrt=1:mu+lambda

if wrt <= mu

imwrite(Result{wrt}, ['Save/G', num2str(g), 'Parent', num2str(wrt), '.jpg']);

else