psnrjava代码 psm代码

求M文件，直接求图像的峰值信噪比（无论灰度和彩色）

close all

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clear all

I=imread('lena.bmp');

J=imnoise(I,'gaussian',0.01,0.005);

A=imread('lena.bmp');

[M,N]=size(A);

B = imread('J.bmp');

a=double(A);

b=double(B);

sum=0;

for i=1:M;

for j=1:N;

sum=sum+(a(i,j)-b(i,j))^2;

end;

end;

mseValue=sum/(M*N);

psnrValue=10*log10(255^2/mseValue);

disp(['输入数据的MSE为:',num2str(mseValue)]);

disp(['输入数据的PSNR为:',num2str(psnrValue)]);

输入数据的MSE为:7915.4387

输入数据的PSNR为:9.1461

以上是MATLAB程序 及其输出结果 M文件可为

function PSNR = PSNR(A,B)

[M,N]=size(A);

x=double(A);

y=double(B);

sum=0;

for i=1:M;

for j=1:N;

sum=sum+(x(i,j)-y(i,j))^2;

end;

end;

mseValue=sum/(M*N);

psnrValue=10*log10(255^2/mseValue);

disp(['输入数据的MSE为:',num2str(mseValue)]);

disp(['输入数据的PSNR为:',num2str(psnrValue)]);

数字音频水印——峰值信噪比PSNR与信噪比SNR的问题。求告之！求代码！

峰值信噪比PSNR

(1)PSNR (Peak signal-to-noise ratio)

常用于图像压缩等领域中，压缩前与压缩后，图像劣化程度的客观评价。

评价结果以dB（对比分贝）为单位来表示。2个图像间，PSNR值越大，趋于无劣化，劣化程度较大时，PSNR值趋于0dB。

不知道你是灰度图像水印还是彩色图像水印，还是音频转成的二维矩阵，我就简单的用灰度水印图像介绍一下；

PSNR的公式是：

、

如上图MSE是原始和编码后图像的之间的均方误差，n表示每个像素的比特数，公式的具体解释和证明去自己找资料吧。

看你代码的形式，应该是matlab

其中n表示的比特数为8比特

function [PSNR, MSE] = psnr(X, Y)

% 计算峰值信噪比PSNR、均方根误差MSE

% 如果输入Y为空，则视为X与其本身来计算PSNR、MSE

if nargin2

D = X;

else

if any(size(X)~=size(Y))

  error('The input size is not equal to each other!');

end

D = X-Y;

end

MSE = sum(D(:).*D(:))/prod(size(X));

PSNR = 10*log10(255^2/MSE);

以下个人观点：我做实验的时候不太喜欢用PSNR，实验结果显示，PSNR 的分数无法和人眼看到的视觉品质完全一致，有可能 PSNR 较高者看起来反而比PSNR 较低者差，语音水印的品质也很成问题，不建议用PSNR，除非你的算法和PSNR很合得来，可以作为参考参数。

下班了,待续...

求基于BP神经网络的图像复原代码，着急用，帮帮我

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_x;c];

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_x;c];

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');