2-D implementation of the ResNet image super resolution architecture.

Creates a keras model of the expanded image super resolution deep learning framework based on the following python implementation:

createResNetSuperResolutionModel2D(
  inputImageSize,
  convolutionKernelSize = c(3, 3),
  numberOfFilters = 64,
  numberOfResidualBlocks = 5,
  numberOfResNetBlocks = 1
)

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.
numberOfResNetBlocks	the number of resNet blocks. Each block will double the upsampling amount.

Value

a keras model for ResNet image super resolution

Details

    \url{https://github.com/titu1994/Image-Super-Resolution}

Author

Tustison NJ

Examples

createResNetSuperResolutionModel2D(c(256, 256, 3))
#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist