求基于BP神经网络的图像复原算法的matlab代码

function Solar_SAE

tic

n = 300

m=20

train_x = []

test_x = []

for i = 1:n

%filename = strcat(['D:\Program Files\MATLAB\R2012a\work\DeepLearn\Solar_SAE\64_64_3train\' num2str(i,'%03d') '.bmp'])

%filename = strcat(['E:\matlab\work\c0\TrainImage' num2str(i,'%03d') '.bmp'])

filename = strcat(['E:\image restoration\3-(' num2str(i) ')-4.jpg'])

b = imread(filename)

%c = rgb2gray(b)

c=b

[ImageRow ImageCol] = size(c)

c = reshape(c,[1,ImageRow*ImageCol])

train_x = [train_xc]

end

for i = 1:m

%filename = strcat(['D:\Program Files\MATLAB\R2012a\work\DeepLearn\Solar_SAE\64_64_3test\' num2str(i,'%03d') '.bmp'])

%filename = strcat(['E:\matlab\work\c0\TestImage' num2str(i+100,'%03d') '-1.bmp'])

filename = strcat(['E:\image restoration\3-(' num2str(i+100) ').jpg'])

b = imread(filename)

%c = rgb2gray(b)

c=b

[ImageRow ImageCol] = size(c)

c = reshape(c,[1,ImageRow*ImageCol])

test_x = [test_xc]

end

train_x = double(train_x)/255

test_x = double(test_x)/255

%train_y = double(train_y)

%test_y = double(test_y)

% Setup and train a stacked denoising autoencoder (SDAE)

rng(0)

%sae = saesetup([4096 500 200 50])

%sae.ae{1}.activation_function = 'sigm'

%sae.ae{1}.learningRate = 0.5

%sae.ae{1}.inputZeroMaskedFraction = 0.0

%sae.ae{2}.activation_function = 'sigm'

%sae.ae{2}.learningRate = 0.5

%%sae.ae{2}.inputZeroMaskedFraction = 0.0

%sae.ae{3}.activation_function = 'sigm'

%sae.ae{3}.learningRate = 0.5

%sae.ae{3}.inputZeroMaskedFraction = 0.0

%sae.ae{4}.activation_function = 'sigm'

%sae.ae{4}.learningRate = 0.5

%sae.ae{4}.inputZeroMaskedFraction = 0.0

%opts.numepochs = 10

%opts.batchsize = 50

%sae = saetrain(sae, train_x, opts)

%visualize(sae.ae{1}.W{1}(:,2:end)')

% Use the SDAE to initialize a FFNN

nn = nnsetup([4096 1500 500 200 50 200 500 1500 4096])

nn.activation_function = 'sigm'

nn.learningRate = 0.03

nn.output = 'linear'% output unit 'sigm' (=logistic), 'softmax' and 'linear'

%add pretrained weights

%nn.W{1} = sae.ae{1}.W{1}

%nn.W{2} = sae.ae{2}.W{1}

%nn.W{3} = sae.ae{3}.W{1}

%nn.W{4} = sae.ae{3}.W{2}

%nn.W{5} = sae.ae{2}.W{2}

%nn.W{6} = sae.ae{1}.W{2}

%nn.W{7} = sae.ae{2}.W{2}

%nn.W{8} = sae.ae{1}.W{2}

% Train the FFNN

opts.numepochs = 30

opts.batchsize = 150

tx = test_x(14,:)

nn1 = nnff(nn,tx,tx)

ty1 = reshape(nn1.a{9},64,64)

nn = nntrain(nn, train_x, train_x, opts)

toc

tic

nn2 = nnff(nn,tx,tx)

toc

tic

ty2 = reshape(nn2.a{9},64,64)

tx = reshape(tx,64,64)

tz = tx - ty2

tz = im2bw(tz,0.1)

%imshow(tx)

%figure,imshow(ty2)

%figure,imshow(tz)

ty = cat(2,tx,ty2,tz)

montage(ty)

filename3 = strcat(['E:\image restoration\3.jpg'])

e=imread(filename3)

f= rgb2gray(e)

f=imresize(f,[64,64])

%imshow(ty2)

f=double (f)/255

[PSNR, MSE] = psnr(ty2,f)

imwrite(ty2,'E:\image restoration\bptest.jpg','jpg')

toc

%visualize(ty)

%[er, bad] = nntest(nn, tx, tx)

%assert(er <0.1, 'Too big error')

1,我用的是matlab R2008,一列代表一个样本。其他版本的不知道

2，matlab中神经网络工具箱就已经很方便了，调用函数和修改参数就可以用了。

3，net_1.trainParam.lr代表学习速率，net_1.trainParam.mc则是动量系数。至于参数的确定，要看具体情况。

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