通过将 Deep Learning Toolbox™ 与 Image Processing Toolbox™ 结合使用，将深度学习应用于图像处理应用。
|Transform batches to augment image data|
|Datastore for extracting random 2-D or 3-D random patches from images or pixel label images|
|Datastore for use with blocks from |
- 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 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).