applyDeepBackProjectionModel: Apply a pretrained deep back projection model for super...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

Apply a pretrained deep back projection model for super resolution.

Description

Helper function for applying a pretrained deep back projection model. Apply a patch-wise trained network to perform super-resolution. Can be applied to variable sized inputs. Warning: This function may be better used on CPU unless the GPU can accommodate the full image size. Warning 2: The global intensity range (min to max) of the output will match the input where the range is taken over all channels.

Usage

applyDeepBackProjectionModel(
  image,
  model,
  targetRange = c(-127.5, 127.5),
  batchSize = 32,
  regressionOrder = NA,
  verbose = FALSE
)

Arguments

image	input image.
model	pretrained model or filename (cf getPretrainedNetwork).
targetRange	a vector defining the c(min, max) of each input image (e.g., -127.5, 127.5). Output images will be scaled back to original intensity. This range should match the mapping used in the training of the network.
batchSize	batch size used for the prediction call.
regressionOrder	if specified, then apply the function regressionMatchImage with polyOrder = regressionOrder.
verbose	If TRUE, show status messages.

Value

super-resolution image upscaled to resolution specified by the network.

Author(s)

Avants BB

Examples

## Not run: 
image <- applyDeepBackProjectionModel( ri( 1 ), getPretrainedNetwork( "dbpn4x" ) )

## End(Not run)

ANTsX/ANTsRNet documentation built on April 23, 2024, 1:24 p.m.