createEnhancedDeepSuperResolutionModel2D: 2-D implementation of the EDSR super resolution architecture.

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the EDSR super resolution architecture.

Description

Creates a keras model of the expanded image super resolution deep learning framework based on EDSR.

Usage

createEnhancedDeepSuperResolutionModel2D(
  inputImageSize,
  convolutionKernelSize = c(3, 3),
  numberOfFilters = 256,
  numberOfResidualBlocks = 32,
  scale = 2,
  numberOfLossFunctions = 1,
  numberOfOutputChannels = 1,
  doBatchNormalization = FALSE,
  interpolation = c("bilinear", "nearest", "conv")
)

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.
convolutionKernelSize	a vector specifying the kernel size for convolution.
numberOfFilters	the number of filters for each encoding layer.
numberOfResidualBlocks	the number of residual blocks.
scale	the upsampling amount, 2, 4 or 8
numberOfLossFunctions	the number of data targets, e.g. 2 for 2 targets
numberOfOutputChannels	the number of ouput channels
doBatchNormalization	boolean for include BN in the residual blocks
interpolation	nearest, bilinear or conv for upscaling block

Value

a keras model for EDSR image super resolution

Author(s)

Tustison NJ, Avants BB

Examples

model = createEnhancedDeepSuperResolutionModel2D(c( 28, 28, 1 ))
rm(model); gc()
model = createEnhancedDeepSuperResolutionModel2D(c( 28, 28, 1 ),
doBatchNormalization = TRUE,
interpolation = "conv", scale = 4)
rm(model); gc()
model = createEnhancedDeepSuperResolutionModel2D(c( 28, 28, 1 ),
doBatchNormalization = TRUE,
numberOfLossFunctions = 2,
interpolation = "conv", scale = 8)
rm(model); gc()

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