createImageSuperResolutionModel2D: 2-D implementation of the image super resolution deep...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the image super resolution deep learning architecture.

Description

Creates a keras model of the image super resolution deep learning framework. based on the paper available here:

Usage

createImageSuperResolutionModel2D(
  inputImageSize,
  convolutionKernelSizes = list(c(9, 9), c(1, 1), c(5, 5)),
  numberOfFilters = c(64, 32)
)

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 list of 2-D vectors specifying the kernel size at each convolution layer. Default values are the same as given in the original paper. The length of kernel size vectors must be 1 greater than the vector length of the number of filters.
numberOfFilters	a vector containing the number of filters for each convolutional layer. Default values are the same as given in the original paper.

Details

    \url{https://arxiv.org/pdf/1501.00092}

This particular implementation is based on the following python implementation:

    \url{https://github.com/titu1994/Image-Super-Resolution}

Value

a keras model for image super resolution

Author(s)

Tustison NJ

Examples

createImageSuperResolutionModel2D(c( 100, 100, 1 ))
gc()

ANTsX/ANTsRNet documentation built on Nov. 21, 2024, 4:07 a.m.