Procesamiento de imágenes mediante deep learning

Realice tareas de procesamiento de imágenes, como eliminar el ruido de la imagen y realice una traducción entre imágenes, mediante técnicas de deep learning que usan redes neuronales profundas (requiere Deep Learning Toolbox™)

Las técnicas de deep learning usan redes neuronales para aprender representaciones útiles de ciertas características directamente a partir de los datos. Por ejemplo, se puede usar una red neuronal previamente entrenada para identificar y eliminar ciertos artefactos de una imagen, como el ruido.

Funciones

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Crear almacenes de datos para el preprocesamiento de imágenes

`augmentedImageDatastore`	Transform batches to augment image data
`blockedImageDatastore`	Datastore for use with blocks from `blockedImage` objects
`denoisingImageDatastore`	Denoising image datastore
`imageDatastore`	Datastore for image data
`randomPatchExtractionDatastore`	Datastore for extracting random 2-D or 3-D random patches from images or pixel label images
`transform`	Transform datastore
`combine`	Combine data from multiple datastores

Aumentar imágenes

`jitterColorHSV`	Randomly alter color of pixels
`randomWindow2d`	Randomly select rectangular region in image
`randomCropWindow3d`	Create randomized cuboidal cropping window
`centerCropWindow2d`	Create rectangular center cropping window
`centerCropWindow3d`	Create cuboidal center cropping window
`Rectangle`	Spatial extents of 2-D rectangular region
`Cuboid`	Spatial extents of 3-D cuboidal region
`randomAffine2d`	Create randomized 2-D affine transformation
`randomAffine3d`	Create randomized 3-D affine transformation
`affineOutputView`	Create output view for warping images
`imerase`	Remove image pixels within rectangular region of interest

Cambiar el tamaño y la forma de las entradas de deep learning

`resize2dLayer`	2-D resize layer
`resize3dLayer`	3-D resize layer
`dlresize`	Resize spatial dimensions of `dlarray` object
`DepthToSpace2DLayer`	Depth to space layer
`SpaceToDepthLayer`	Space to depth layer
`depthToSpace`	Rearrange `dlarray` data from depth dimension into spatial blocks
`spaceToDepth`	Rearrange spatial blocks of `dlarray` data along depth dimension

Crear redes de deep learning

`encoderDecoderNetwork`	Create encoder-decoder network
`blockedNetwork`	Create network with repeating block structure
`pretrainedEncoderNetwork`	Create encoder network from pretrained network
`cycleGANGenerator`	Create CycleGAN generator network for image-to-image translation
`patchGANDiscriminator`	Create PatchGAN discriminator network
`pix2pixHDGlobalGenerator`	Create pix2pixHD global generator network
`addPix2PixHDLocalEnhancer`	Add local enhancer network to pix2pixHD generator network
`unitGenerator`	Create unsupervised image-to-image translation (UNIT) generator network
`unitPredict`	Perform inference using unsupervised image-to-image translation (UNIT) network

Eliminar el ruido de una imagen

`denoiseImage`	Denoise image using deep neural network
`denoisingNetwork`	Obtener una red para eliminar el ruido de una imagen
`dnCNNLayers`	Get denoising convolutional neural network layers

Temas

Preprocesar datos de imágenes para deep learning

Get Started with Image Preprocessing and Augmentation for Deep Learning

Preprocess data with deterministic operations such as normalization or color space conversion, or augment training data with randomized operations such as random cropping or color jitter.
Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
Prepare Datastore for Image-to-Image Regression (Deep Learning Toolbox)
This example shows how to prepare a datastore for training an image-to-image regression network using the transform and combine functions of ImageDatastore.
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.

Crear redes neuronales para aplicaciones de procesamiento de imágenes

Train and Apply Denoising Neural Networks
Use a pretrained neural network to remove Gaussian noise from a grayscale image, or train your own network using predefined layers.
Create Modular Neural Networks
You can create and customize deep learning networks that follow a modular pattern with repeating groups of layers, such as U-Net and cycleGAN.
Get Started with GANs for Image-to-Image Translation
GAN networks can transfer the styles and characteristics from one set of images to the scene content of other images.
Pretrained Deep Neural Networks (Deep Learning Toolbox)
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.
List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB^®.

Deep learning en MATLAB

Deep Learning in MATLAB (Deep Learning Toolbox)
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
This example shows how to train a semantic segmentation network using deep learning.

Ejemplos destacados

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.

Procesamiento de imágenes mediante deep learning

Funciones

Crear almacenes de datos para el preprocesamiento de imágenes

Aumentar imágenes

Cambiar el tamaño y la forma de las entradas de deep learning

Crear redes de deep learning

Eliminar el ruido de una imagen

Temas

Preprocesar datos de imágenes para deep learning

Crear redes neuronales para aplicaciones de procesamiento de imágenes

Deep learning en MATLAB

Ejemplos destacados

Increase Image Resolution Using Deep Learning

Unsupervised Day-to-Dusk Image Translation Using UNIT

Detect Image Anomalies Using Explainable One-Class Classification Neural Network

Classify Defects on Wafer Maps Using Deep Learning

Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings

Unsupervised Medical Image Denoising Using CycleGAN

Unsupervised Medical Image Denoising Using UNIT

JPEG Image Deblocking Using Deep Learning

Image Processing Operator Approximation Using Deep Learning

Develop Camera Processing Pipeline Using Deep Learning

Brighten Extremely Dark Images Using Deep Learning

Classify Tumors in Multiresolution Blocked Images

3-D Brain Tumor Segmentation Using Deep Learning

Semantic Segmentation of Multispectral Images Using Deep Learning

Neural Style Transfer Using Deep Learning

Remove Noise from Color Image Using Pretrained Neural Network

Quantify Image Quality Using Neural Image Assessment