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## Remove Noise from Color Image Using Pretrained Neural Network

This example shows how to remove Gaussian noise from an RGB image. Convert the noisy image to the L*a*b* color space, and remove noise on the luminance channel L* by using a pretrained denoising neural network.

In practice, image color channels frequently have correlated noise. You will have better denoising results if you train a denoising network on color images. For more information, see Train and Apply Denoising Neural Networks.

Read a color image into the workspace and convert the data to type `double`. Display the image.

```RGB = imread('lighthouse.png'); RGB = im2double(RGB); figure imshow(RGB) title('Pristine Image')```

Add Gaussian noise with a variance of 0.01 to the image. `imnoise` adds noise to each color channel independently. Display the noisy image.

```noisyRGB = imnoise(RGB,'gaussian',0,0.01); figure imshow(noisyRGB) title('Noisy Image')```

Load the pretrained denoising neural network.

`net = denoisingNetwork('dncnn');`

Convert the image to the L*a*b* color space.

`LAB = rgb2lab(RGB);`

Noise is primarily in the luminance channel. Remove the noise from this channel only, by using the pretrained denoising neural network.

`LAB(:,:,1) = denoiseImage(LAB(:,:,1),net);`

Convert the image back to the RGB color space.

`denoisedRGB = lab2rgb(LAB);`

Display the denoised image.

```figure 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,RGB); 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,RGB); fprintf('\n The PSNR value of the denoised image is %0.4f.',denoisedPSNR);```
``` The PSNR value of the denoised image is 53.7825. ```

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,RGB); 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,RGB); fprintf('\n The SSIM value of the denoised image is %0.4f.',denoisedSSIM);```
``` The SSIM value of the denoised image is 0.9999. ```