matlab实现ADnet去噪 详细代码演示
时间: 2023-12-13 12:04:49 浏览: 36
ADnet是一种基于深度学习的图像去噪算法,它能够有效地去除图像中的噪点和伪影。下面是Matlab实现ADnet去噪的详细代码演示。
首先,我们需要加载图像并添加高斯噪声:
```matlab
image = imread('lena.png');
noisy_image = imnoise(image, 'gaussian', 0, 0.01);
```
接下来,我们需要定义ADnet模型。这里我们使用一个三层的卷积神经网络,每一层都包含卷积、批归一化和ReLU激活函数。
```matlab
layers = [
imageInputLayer([256 256 1])
convolution2dLayer(3, 32, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 64, 'Padding', 'same', 'Stride', 2)
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 1, 'Padding', 'same')
regressionLayer
];
```
然后,我们需要定义训练选项,包括学习率、最大训练轮数和优化器等。
```matlab
options = trainingOptions('adam', ...
'InitialLearnRate', 0.001, ...
'MaxEpochs', 30, ...
'MiniBatchSize', 64, ...
'Shuffle', 'every-epoch', ...
'Verbose', false, ...
'Plots', 'training-progress');
```
接下来,我们使用训练数据(干净图像和带噪图像)来训练ADnet模型。
```matlab
clean_images = imageDatastore('clean_images');
noisy_images = imageDatastore('noisy_images');
training_data = combine(clean_images, noisy_images);
adnet = trainNetwork(training_data, layers, options);
```
最后,我们使用训练好的ADnet模型对带噪图像进行去噪处理。
```matlab
denoised_image = predict(adnet, noisy_image);
```
完整的代码如下:
```matlab
% Load image and add noise
image = imread('lena.png');
noisy_image = imnoise(image, 'gaussian', 0, 0.01);
% Define ADnet model
layers = [
imageInputLayer([256 256 1])
convolution2dLayer(3, 32, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 64, 'Padding', 'same', 'Stride', 2)
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 1, 'Padding', 'same')
regressionLayer
];
% Define training options
options = trainingOptions('adam', ...
'InitialLearnRate', 0.001, ...
'MaxEpochs', 30, ...
'MiniBatchSize', 64, ...
'Shuffle', 'every-epoch', ...
'Verbose', false, ...
'Plots', 'training-progress');
% Train ADnet model
clean_images = imageDatastore('clean_images');
noisy_images = imageDatastore('noisy_images');
training_data = combine(clean_images, noisy_images);
adnet = trainNetwork(training_data, layers, options);
% Apply ADnet to noisy image
denoised_image = predict(adnet, noisy_image);
% Display results
subplot(1, 3, 1)
imshow(image)
title('Original')
subplot(1, 3, 2)
imshow(noisy_image)
title('Noisy')
subplot(1, 3, 3)
imshow(denoised_image)
title('Denoised')
```