Build a starlet-based segmentation mask from a spectral cube

Usage

build_starlet_mask(
  input,
  collapse_fn = collapse_white_light,
  pretransform = "none",
  starlet_J = 5,
  starlet_scales = 2:5,
  include_coarse = FALSE,
  denoise_k = 2.5,
  mode = c("soft", "hard"),
  positive_only = TRUE,
  clean_mask = FALSE,
  min_mask_area = 1L,
  close_size = 1L,
  open_size = 1L,
  keep_largest = FALSE
)

Arguments

input: 3-D array or FITS-like list with imDat.
collapse_fn: Function used to collapse the cube to a 2-D image.
pretransform: Optional spectral pretransform applied to the cube before the white-light collapse and starlet decomposition. This is useful when the photometric mask should be derived from a transformed representation rather than the original flux cube.
starlet_J: Number of starlet scales.
starlet_scales: Scales to reconstruct.
include_coarse: Logical; include the coarse plane in the reconstruction.
denoise_k: Optional starlet denoising threshold.
mode: Thresholding mode for starlet reconstruction.
positive_only: Logical; keep only positive reconstruction values in the mask.
clean_mask: Logical; apply optional support-mask cleanup.
min_mask_area: Minimum connected-component area retained when clean_mask = TRUE.
close_size: Binary closing brush size used when clean_mask = TRUE. Use 1 to skip closing.
open_size: Binary opening brush size used when clean_mask = TRUE. Use 1 to skip opening.
keep_largest: Logical; keep only the largest connected component after area filtering when clean_mask = TRUE.

Value

A list with the collapsed image, decomposition, reconstruction, and logical mask.