Image Processing
Apply deep learning to image processing applications by using Deep Learning Toolbox™ together with Image Processing Toolbox™.
Functions
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 |
Topics
- 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.
- 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.
- Augment Images for Deep Learning (Image Processing Toolbox)
Perform common kinds of randomized image augmentation such as image warping and cropping, color adjustments, and adding synthetic distortions.
- Preprocess Volumes for Deep Learning (Image Processing Toolbox)
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).
Featured Examples
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.
Image Processing Operator Approximation Using Deep Learning
Approximate an image filtering operation using a multiscale context aggregation network (CAN).
Develop Camera Processing Pipeline Using Deep Learning
Convert RAW camera data to an aesthetically pleasing color image using a U-Net.
Brighten Extremely Dark Images Using Deep Learning
Recover brightened RGB images from RAW camera data collected in extreme low-light conditions using a U-Net.
Unsupervised Day-to-Dusk Image Translation Using UNIT
Translate images between daytime and dusk lighting conditions using an unsupervised image-to-image translation network (UNIT).
Quantify Image Quality Using Neural Image Assessment
Analyze the aesthetic quality of images using a Neural Image Assessment (NIMA) convolutional neural network (CNN).
JPEG Image Deblocking Using Deep Learning
Reduce JPEG compression artifacts in an image using a denoising convolutional neural network (DnCNN).
3-D Brain Tumor Segmentation Using Deep Learning
Perform semantic segmentation of brain tumors from 3-D medical images.
Classify Tumors in Multiresolution Blocked Images
Classify multiresolution whole slide images (WSIs) that might not fit in memory using an Inception-v3 deep neural network.
Semantic Segmentation of Multispectral Images Using Deep Learning
Perform semantic segmentation of a multispectral image with seven channels using U-Net.
Neural Style Transfer Using Deep Learning
Apply the stylistic appearance of one image to the scene content of a second image using a pretrained VGG-19 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].
Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection
Detect ships from Sentinel-1 C Band SAR Data using YOLOX object detection.
