createDeepDenoiseSuperResolutionModel2D: 2-D implementation of the deep denoise image super resolution...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

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

Description

Creates a keras model of the expanded image super resolution deep learning framework based on the following python implementation:

Usage

createDeepDenoiseSuperResolutionModel2D(
  inputImageSize,
  layers = 2,
  lowestResolution = 64,
  convolutionKernelSize = c(3, 3),
  poolSize = c(2, 2),
  strides = c(2, 2)
)

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.
layers	number of architecture layers.
lowestResolution	number of filters at the beginning and end of the architecture.
convolutionKernelSize	2-D vector defining the kernel size during the encoding path
poolSize	2-D vector defining the region for each pooling layer.
strides	2-D vector describing the stride length in each direction.

Details

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

Value

a keras model for image super resolution

Author(s)

Tustison NJ

Examples

## Not run: 
createDeepDenoiseSuperResolutionModel2D(c(256L, 256L, 3L))

## End(Not run)

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