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denoiseImage

Denoise image using deep neural network

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Syntax

B = denoiseImage(A,net)

Description

B = denoiseImage(A,net) removes noise from noisy image A using a denoising deep neural network specified by net.

This function requires Deep Learning Toolbox™.

example

Examples

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This example uses:

Open Live Script

Load the pretrained denoising convolutional neural network, "DnCNN".

net = denoisingNetwork("DnCNN");

Load a grayscale image into the workspace, then create a noisy version of the image.

I = imread("cameraman.tif");
noisyI = imnoise(I,"gaussian",0,0.01);

Display the two images as a montage.

montage({I,noisyI})
title("Original Image (Left) and Noisy Image (Right)")

Figure contains an axes object. The hidden axes object with title Original Image (Left) and Noisy Image (Right) contains an object of type image.

Remove noise from the noisy image, then display the result.

denoisedI = denoiseImage(noisyI,net);
imshow(denoisedI)
title("Denoised Image")

Figure contains an axes object. The hidden axes object with title Denoised Image contains an object of type image.

Input Arguments

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Noisy image, specified as a single 2-D image or a stack of 2-D images. A can be:

  • A 2-D grayscale image with size m-by-n.

  • A 2-D multichannel image with size m-by-n-by-c, where c is the number of image channels. For example, c is 3 for RGB images, and 4 for four-channel images such as RGB images with an infrared channel.

  • A stack of equally-sized 2-D images. In this case, A has size m-by-n-by-c-by-p, where p is the number of images in the stack.

Data Types: single | double | uint8 | uint16

Denoising deep neural network, specified as a dlnetwork (Deep Learning Toolbox) object. The network should be trained on images with the same number of color channels as A. The input size of the network does not need to match the size of A.

If the noisy image or stack of images A has only one channel and has Gaussian noise, then you can get a pretrained network by using the denoisingNetwork function. For more information about creating a denoising network, see Train and Apply Denoising Neural Networks.

Output Arguments

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Denoised image, returned as a single 2-D image or a stack of 2-D images. B has the same size and data type as A.

Version History

Introduced in R2017b

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See Also

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