Image Processing Using Deep Learning

Extend deep learning workflows with image processing applications

Apply deep learning to image processing applications by using Deep Learning Toolbox™ together with Image Processing Toolbox™.

Functions

`augmentedImageDatastore`	Transform batches to augment image data
`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`对象

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.
Augment Images for Deep Learning Workflows Using Image Processing Toolbox
This example shows how MATLAB® and Image Processing Toolbox™ can perform common kinds of image augmentation as part of deep learning workflows.
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)
GAN networks can transfer the styles and characteristics from one set of images to the scene content of other images.

Featured Examples

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.

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图像处理操作or Approximation Using Deep Learning

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

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Develop Camera Processing Pipeline Using Deep Learning

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

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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.

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Detect Image Anomalies Using Explainable One-Class Classification Neural Network

Train an anomaly detector for visual inspection of pill images.

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Classify Defects on Wafer Maps Using Deep Learning

八个类型进行分类的制造缺陷afer maps using a simple convolutional neural network (CNN).

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Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings

Train a similarity-based anomaly detector using one-class learning of feature embeddings extracted from a pretrained ResNet-18 convolutional neural network.

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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).

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Unsupervised Medical Image Denoising Using CycleGAN

Generate high-quality high-dose computed tomography (CT) images from noisy low-dose CT images using a CycleGAN neural network.

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Unsupervised Medical Image Denoising Using UNIT

Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.

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Quantify Image Quality Using Neural Image Assessment

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

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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.

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3-D Brain Tumor Segmentation Using Deep Learning

Train a 3-D U-Net neural network and perform semantic segmentation of brain tumors from 3-D medical images.

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Semantic Segmentation of Multispectral Images Using Deep Learning

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

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JPEG Image Deblocking Using Deep Learning

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

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Remove Noise from Color Image Using Pretrained Neural Network

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

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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.

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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].

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