Bildverarbeitung
Wenden Sie Deep Learning bei Bildverarbeitungsanwendungen an, indem Sie die Deep Learning Toolbox™ zusammen mit der Image Processing Toolbox™ verwenden.
Funktionen
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 (Seit R2021a) |
Themen
- 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).
Enthaltene Beispiele
Segment Objects Using Segment Anything Model (SAM) in Image Segmenter
Interactively segment objects or automatically segment the entire image using the Segment Anything Model (SAM) in the Image Segmenter app.
Increase Image Resolution Using Deep Learning
Create a high-resolution image from a low-resolution image using a very-deep super-resolution (VDSR) neural network.
Train Fast Style Transfer Network
Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].
