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Procesamiento de imágenes mediante deep learning

Amplíe flujos de trabajo de deep learning con aplicaciones de procesamiento de imágenes

Aplique deep learning en aplicaciones de procesamiento de imágenes con Deep Learning Toolbox™ y Image Processing Toolbox™ y Medical Imaging Toolbox™.

Apps

Medical Image Labeler

Interactively explore, label, and publish animations of 2-D or 3-D medical image data

Funciones

`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` objects

Temas

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 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)
Transfer styles and characteristics from one set of images to the scene content of other images by using generative adversarial networks (GANs).
Create Datastores for Medical Image Semantic Segmentation (Medical Imaging Toolbox)
Create datastores that contain images and pixel label data from a groundTruthMedical object for training semantic segmentation deep learning networks.

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.

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Image Processing Operator 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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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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Segment Lungs from CT Scan Using Pretrained Neural Network

Import a pretrained ONNX™ (Open Neural Network Exchange) 3-D U-Net [1] and use it to perform semantic segmentation of the left and right lungs from a 3-D chest CT scan. Semantic segmentation associates each voxel in a 3-D image with a class label. In this example, you classify each voxel in a test data set as belonging to the left lung or right lung. For more information about semantic segmentation, see Semantic Segmentation (Computer Vision Toolbox).

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Brain MRI Segmentation Using Pretrained 3-D U-Net Network

Segment a brain MRI using a deep neural network.

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Breast Tumor Segmentation from Ultrasound Using Deep Learning

Perform semantic segmentation of breast tumors from 2-D ultrasound images using a deep neural network.

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3-D Brain Tumor Segmentation Using Deep Learning

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 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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