图像处理
通过将 Deep Learning Toolbox™ 与 Image Processing Toolbox™ 结合使用，将深度学习应用于图像处理应用。
函数
randomPatchExtractionDatastore | Datastore for extracting random 2-D or 3-D random patches from images or pixel label images |
blockedImageDatastore | 用于 blockedImage 对象中的图像块的数据存储 (自 R2021a 起) |
主题
- 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.
- 预处理图像以进行深度学习
了解如何调整图像大小以进行训练、预测和分类，以及如何使用数据增强、变换和专用数据存储对图像进行预处理。
- 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).
精选示例
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.
使用深度学习增加图像分辨率
此示例说明如何使用超深超分辨率 (VDSR) 神经网络从低分辨率图像创建高分辨率图像。
使用深度学习进行图像处理算子逼近
此示例说明如何使用多尺度上下文聚合网络 (CAN) 逼近图像滤波运算。
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).
使用深度学习进行 JPEG 图像去块
此示例说明如何使用去噪卷积神经网络 (DnCNN) 来减少图像中的 JPEG 压缩伪影。
使用深度学习进行三维脑肿瘤分割
此示例说明如何基于三维医学图像执行脑肿瘤的语义分割。
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.
使用深度学习对多光谱图像进行语义分割
此示例说明如何使用 U-Net 对包含七个通道的多光谱图像执行语义分割。
使用深度学习的神经样式迁移
此示例说明如何使用预训练的 VGG-19 网络将一个图像的样式外观应用于另一个图像的场景内容。
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.
