Deep Learning for Image Processing
Perform image processing tasks, such as removing image noise and
performing image-to-image translation, using deep neural networks (requires
Deep Learning 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 artifacts like noise from images.
Functions
Topics
Preprocess Image Data for Deep Learning
- Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.
- Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications. - Prepare Datastore for Image-to-Image Regression (Deep Learning Toolbox)
This example shows how to prepare a datastore for training an image-to-image regression network using thetransform
andcombine
functions ofImageDatastore
.
Create Neural Networks for Image Processing Applications
- 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. - Create Modular Neural Networks
You can create and customize deep learning networks that follow a modular pattern with repeating groups of layers, such as U-Net and cycleGAN. - Get Started with GANs for Image-to-Image Translation
Transfer styles and characteristics from one set of images to the scene content of other images by using generative adversarial networks (GANs). - Pretrained Deep Neural Networks (Deep Learning Toolbox)
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction. - List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB®.
Deep Learning in MATLAB
- Deep Learning in MATLAB (Deep Learning 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. - Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
This example shows how to segment an image using a semantic segmentation network.