createDenoisingAutoEncoderSuperResolutionModel2D: 2-D implementation of the denoising autoencoder image super...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the denoising autoencoder image super resolution architecture.

Description

2-D implementation of the denoising autoencoder image super resolution architecture.

Usage

createDenoisingAutoEncoderSuperResolutionModel2D(
  inputImageSize,
  convolutionKernelSizes = list(c(3, 3), c(5, 5)),
  numberOfEncodingLayers = 2,
  numberOfFilters = 64
)

Arguments

inputImageSize	Used for specifying the input tensor shape. The shape (or dimension) of that tensor is the image dimensions followed by the number of channels (e.g., red, green, and blue). The batch size (i.e., number of training images) is not specified a priori.
convolutionKernelSizes	a 2-element list of 2-D vectors specifying the kernel size at each convolution layer. The first element is the kernel size of the encoding layers and the 2nd element is the kernel size of the final convolution layer.
numberOfEncodingLayers	the number of encoding layers.
numberOfFilters	the number of filters for each encoding layer.

Value

a keras model for image super resolution

Author(s)

Tustison NJ

Examples

createDenoisingAutoEncoderSuperResolutionModel2D(c( 28, 28, 1 ))

ANTsX/ANTsRNet documentation built on April 18, 2024, 8 a.m.