Procesamiento de imágenes mediante deep learning
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
Temas
Preprocesar datos de imágenes para deep learning
- Get Started with Image Preprocessing and Augmentation for Deep Learning
Preprocess data for deep learning applications with deterministic operations such as resizing, or augment training data with randomized operations such as random cropping.
- 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 thetransformandcombinefunctions ofImageDatastore.
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
Transfer styles and characteristics from one set of images to the scene content of other images by using generative adversarial networks (GANs). - Redes neuronales profundas preentrenadas (Deep Learning Toolbox)
Aprenda a descargar y utilizar redes neuronales convolucionales preentrenadas para clasificación, transferencia del aprendizaje y extracción de características. - Lista de capas de deep learning (Deep Learning Toolbox)
Descubra todas las capas de deep learning de MATLAB®.
Deep learning en MATLAB
- Deep learning en MATLAB (Deep Learning Toolbox)
Descubra las prestaciones de deep learning en MATLAB utilizando redes neuronales convolucionales para clasificación y regresión, incluidas redes preentrenadas y transferencia del aprendizaje, así como entrenamiento en unidades de procesamiento gráfico (GPU), unidades CPU, clusters y nubes. - Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
This example shows how to segment an image using a semantic segmentation network.
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.
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).
Detect Image Anomalies Using Explainable FCDD Network
Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.
Classify Defects on Wafer Maps Using Deep Learning
Classify eight types of manufacturing defects on wafer maps using a simple convolutional neural network (CNN).
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.
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.
Unsupervised Medical Image Denoising Using UNIT
Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.
JPEG Image Deblocking Using Deep Learning
Reduce JPEG compression artifacts in an image using a denoising convolutional neural network (DnCNN).
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.
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.
3-D Brain Tumor Segmentation Using Deep Learning
Perform semantic segmentation of brain tumors from 3-D medical images.
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.
Remove Noise from Color Image Using Pretrained Neural Network
Remove Gaussian noise from an RGB image using a denoising convolutional neural network.
Quantify Image Quality Using Neural Image Assessment
Analyze the aesthetic quality of images using a Neural Image Assessment (NIMA) convolutional neural network (CNN).
