This example shows how to remove Gaussian noise from an RGB image using a denoising convolutional neural network.
Read a color image into the workspace and convert the data to data type
double. Display the pristine color image.
pristineRGB = imread('lighthouse.png'); pristineRGB = im2double(pristineRGB); imshow(pristineRGB) title('Pristine Image')
Add zero-mean Gaussian white noise with a variance of 0.01 to the image. The
imnoise function adds noise to each color channel independently. Display the noisy color image.
noisyRGB = imnoise(pristineRGB,'gaussian',0,0.01); imshow(noisyRGB) title('Noisy Image')
The pretrained denoising convolutional neural network, DnCNN, operates on single-channel images. Split the noisy RGB image into its three individual color channels.
[noisyR,noisyG,noisyB] = imsplit(noisyRGB);
Load the pretrained DnCNN network.
net = denoisingNetwork('dncnn');
Use the DnCNN network to remove noise from each color channel.
denoisedR = denoiseImage(noisyR,net); denoisedG = denoiseImage(noisyG,net); denoisedB = denoiseImage(noisyB,net);
Recombine the denoised color channels to form the denoised RGB image. Display the denoised color image.
denoisedRGB = cat(3,denoisedR,denoisedG,denoisedB); imshow(denoisedRGB) title('Denoised Image')
Calculate the peak signal-to-noise ratio (PSNR) for the noisy and denoised images. A larger PSNR indicates that noise has a smaller relative signal, and is associated with higher image quality.
noisyPSNR = psnr(noisyRGB,pristineRGB); fprintf('\n The PSNR value of the noisy image is %0.4f.',noisyPSNR);
The PSNR value of the noisy image is 20.6395.
denoisedPSNR = psnr(denoisedRGB,pristineRGB); fprintf('\n The PSNR value of the denoised image is %0.4f.',denoisedPSNR);
The PSNR value of the denoised image is 29.6857.
Calculate the structural similarity (SSIM) index for the noisy and denoised images. An SSIM index close to 1 indicates good agreement with the reference image, and higher image quality.
noisySSIM = ssim(noisyRGB,pristineRGB); fprintf('\n The SSIM value of the noisy image is %0.4f.',noisySSIM);
The SSIM value of the noisy image is 0.7393.
denoisedSSIM = ssim(denoisedRGB,pristineRGB); fprintf('\n The SSIM value of the denoised image is %0.4f.',denoisedSSIM);
The SSIM value of the denoised image is 0.9507.
In practice, image color channels frequently have correlated noise. To remove correlated image noise, first convert the RGB image to a color space with a luminance channel, such as the L*a*b* color space. Remove noise on the luminance channel only, then convert the denoised image back to the RGB color space.