3-D implementation of the denoising autoencoder image super resolution architecture.

createDenoisingAutoEncoderSuperResolutionModel3D(
  inputImageSize,
  convolutionKernelSizes = list(c(3, 3, 3), c(5, 5, 5)),
  numberOfEncodingLayers = 2,
  numberOfFilters = 64
)

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.
convolutionKernelSizes	a 2-element list of 3-D vectors specifying the kernel size at each convolution layer. The first element is the kernel size of the encoding layers and the 2nd element is the kernel size of the final convolution layer.
numberOfEncodingLayers	the number of encoding layers.
numberOfFilters	the number of filters for each encoding layer.

Value

a keras model for image super resolution

Author

Tustison NJ

Examples

createDenoisingAutoEncoderSuperResolutionModel3D(c( 28, 28, 28, 1 ))
#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist
gc()
#>           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
#> Ncells 2504613 133.8    4570014 244.1         NA  4570014 244.1
#> Vcells 4409936  33.7   10146329  77.5      65536  6714963  51.3