딥러닝을 사용한 영상 처리

이 예제에서는 VDSR(Very-Deep Super-Resolution) 신경망을 사용하여 하나의 저해상도 영상에서 고해상도 영상을 만드는 방법을 보여줍니다.

Approximate an image filtering operation using a multiscale context aggregation network (CAN).

Convert RAW camera data to an aesthetically pleasing color image using a U-Net.

Recover brightened RGB images from RAW camera data collected in extreme low-light conditions using a U-Net.

Translate images between daytime and dusk lighting conditions using an unsupervised image-to-image translation network (UNIT).

Generate high-quality high-dose computed tomography (CT) images from noisy low-dose CT images using a CycleGAN neural network.

Generate high-quality computed tomography (CT) images from noisy low-dose CT images using a UNIT neural network.

Analyze the aesthetic quality of images using a Neural Image Assessment (NIMA) convolutional neural network (CNN).

Classify multiresolution whole slide images (WSIs) that might not fit in memory using an Inception-v3 deep 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 의미론적 분할 (Computer Vision Toolbox).

Segment a brain MRI using a deep neural network.

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

이 예제에서는 3차원 의료 영상에서 뇌종양의 의미론적 분할을 수행하는 방법을 다룹니다.

이 예제에서는 U-Net을 사용하여 7가지 채널을 갖는 다중분광 영상에 대한 의미론적 분할을 수행하는 방법을 보여줍니다.

Reduce JPEG compression artifacts in an image using a denoising convolutional neural network (DnCNN).

Remove Gaussian noise from an RGB image using a denoising convolutional neural network.

이 예제에서는 사전 훈련된 VGG-19 신경망을 사용하여 한 영상의 스타일을 두 번째 영상의 장면 콘텐츠에 적용하는 방법을 보여줍니다.

Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].