applySuperResolutionModelPatch: applySuperResolutionModelPatch

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

applySuperResolutionModelPatch

Description

Apply pretrained super-resolution network by stitching together patches.

Usage

applySuperResolutionModelPatch(
  image,
  model,
  targetRange,
  lowResolutionPatchSize = 128,
  strideLength = 16,
  batch_size = 32,
  mask,
  verbose = FALSE
)

Arguments

image	input image
model	model object or filename see getPretrainedNetwork
targetRange	a vector defining min max of each the input image, eg -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.
lowResolutionPatchSize	size of patches to upsample
strideLength	voxel/pixel steps between patches
batch_size	for prediction call
mask	restrict intensity rescaling parameters within the mask
verbose	If TRUE, show status messages

Details

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 patch 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.

Value

image upscaled to resolution provided by network

Author(s)

Avants BB

Examples

## Not run: 
library(ANTsRCore)
library( keras )
orig_img = antsImageRead( getANTsRData( "r16" ) )
# input needs to be 48x48
model = createDeepBackProjectionNetworkModel2D( list(NULL,NULL, 1) )
img = resampleImage(orig_img, resampleParams = rep(256/48, 2))
simg <- applySuperResolutionModelPatch( img,
 model = model, lowResolutionPatchSize = 8, strideLength = 2)
simgm <- applySuperResolutionModelPatch( img, mask = getMask( img ),
 model = model, lowResolutionPatchSize = 8, strideLength = 2)
plot( orig_img )
plot( img )
plot( simg )
plot( simgm )

## End(Not run)

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