Image Processing and Computer Vision

Extend deep learning workflows with image processing, computer vision, medical imaging, lidar, and automated driving applications

Use Deep Learning Toolbox™ to incorporate deep learning in image processing, computer vision, medical imaging, lidar processing, and automated driving applications.

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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Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network

Segment 2-D cardiac MRI images using U-Net, and explore predictions using Grad-CAM explainability maps.

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Lidar Object Detection Using Complex-YOLO v4 Network

Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

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Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

Use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).

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