Traitement d'images

Enrichir des workflows de Deep Learning avec des applications de traitement d'images

Appliquez le Deep Learning à des applications de traitement d’images en utilisant Deep Learning Toolbox™ avec Image Processing Toolbox™.

Fonctions

`randomPatchExtractionDatastore`	Datastore for extracting random 2-D or 3-D random patches from images or pixel label images
`blockedImageDatastore`	Datastore for use with blocks from `blockedImage` objects (depuis R2021a)

Preprocess Data for Domain-Specific Deep Learning Applications
Perform deterministic or randomized data processing for domains such as image processing, object detection, semantic segmentation, signal and audio processing, and text analytics.
Augment Images for Deep Learning Workflows
This example shows how you can perform common kinds of randomized image augmentation such as geometric transformations, cropping, and adding noise.
Preprocess Images for Deep Learning
Learn how to resize images for training, prediction, and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.
Preprocess Volumes for Deep Learning
Read and preprocess volumetric image and label data for 3-D deep learning.
Preprocess Multiresolution Images for Training Classification Network (Image Processing Toolbox)
This example shows how to prepare datastores that read and preprocess multiresolution whole slide images (WSIs) that might not fit in memory.
Get Started with GANs for Image-to-Image Translation (Image Processing Toolbox)
Transfer styles and characteristics from one set of images to the scene content of other images by using generative adversarial networks (GANs).

Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network.

Approximate an image filtering operation using a multiscale context aggregation network (CAN).

Convert RAW camera data to an aesthetically pleasing color image using a U-Net.

Recover brightened RGB images from RAW camera data collected in extreme low-light conditions using a U-Net.

Translate images between daytime and dusk lighting conditions using an unsupervised image-to-image translation network (UNIT).

Analyze the aesthetic quality of images using a Neural Image Assessment (NIMA) convolutional neural network (CNN).

Classify multiresolution whole slide images (WSIs) that might not fit in memory using an Inception-v3 deep neural network.

Perform semantic segmentation of a multispectral image with seven channels using U-Net.

Reduce JPEG compression artifacts in an image using a denoising convolutional neural network (DnCNN).

Remove Gaussian noise from an RGB image using a denoising convolutional neural network.

Apply the stylistic appearance of one image to the scene content of a second image using a pretrained VGG-19 network.

Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].

Detect ships from Sentinel-1 C Band SAR Data using YOLOX object detection.