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Deep Learning for Image Processing

Perform image processing tasks, such as removing image noise and creating high-resolution images from low-resolutions images, using convolutional neural networks (requires Neural Network Toolbox™)

Deep learning uses neural networks to learn useful representations of features directly from data. For example, you can use a pretrained neural network to identify and remove Gaussian noise from images.


denoiseImageDenoise image using deep neural network
denoisingNetworkGet image denoising network
denoisingImageDatastoreDenoising image datastore
dnCNNLayersGet denoising convolutional neural network layers


Denoising Using Deep Learning

Train and Apply Denoising Neural Networks

Use a pretrained neural network to remove Gaussian noise from a grayscale image, or train your own network using predefined layers.

Remove Noise from Color Image Using Pretrained Neural Network

This example shows how to remove Gaussian noise from an RGB image by using a pretrained denoising neural network on each color channel independently.

Deep Learning Applications in MATLAB

Deep Learning in MATLAB (Neural Network Toolbox)

Discover deep learning capabilities in MATLAB® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.

Pretrained Convolutional Neural Networks (Neural Network Toolbox)

Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

Semantic Segmentation Using Deep Learning (Computer Vision System Toolbox)

This example shows how to train a semantic segmentation network using deep learning.

Object Detection Using Deep Learning (Computer Vision System Toolbox)

This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).

Featured Examples

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