R/deepBackProjectionUtilities.R

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

#' Apply a pretrained deep back projection model for super resolution.
#'
#' 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.
#'
#' @param image input image.
#' @param model pretrained model or filename (cf \code{getPretrainedNetwork}).
#' @param targetRange a vector defining the \code{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.
#' @param batchSize batch size used for the prediction call.
#' @param regressionOrder if specified, then apply the function
#'                        \code{regressionMatchImage} with
#'                        \code{polyOrder = regressionOrder}.
#' @param verbose If \code{TRUE}, show status messages.
#' @return super-resolution image upscaled to resolution specified by the network.
#' @author Avants BB
#' @examples
#' \dontrun{
#' image <- applyDeepBackProjectionModel( ri( 1 ), getPretrainedNetwork( "dbpn4x" ) )
#' }
# @export applyDeepBackProjectionModel
applyDeepBackProjectionModel <- function( image, model,
  targetRange = c( -127.5, 127.5 ), batchSize = 32, regressionOrder = NA,
  verbose = FALSE )
{


}