LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
|
Functions | |
np.ndarray | bspline_convolve (np.ndarray image, int scale) |
int | get_starlet_scales (Sequence[int] image_shape, int|None scales=None) |
np.ndarray | starlet_transform (np.ndarray image, int|None scales=None, int generation=2, Callable|None convolve2d=None) |
np.ndarray | multiband_starlet_transform (np.ndarray image, int|None scales=None, int generation=2, Callable|None convolve2d=None) |
np.ndarray | starlet_reconstruction (np.ndarray starlets, int generation=2, Callable|None convolve2d=None) |
np.ndarray | multiband_starlet_reconstruction (np.ndarray starlets, int generation=2, Callable|None convolve2d=None) |
np.ndarray | get_multiresolution_support (np.ndarray image, np.ndarray starlets, float sigma, float sigma_scaling=3, float epsilon=1e-1, int max_iter=20, str image_type="ground") |
np.ndarray | apply_wavelet_denoising (np.ndarray image, float|None sigma=None, float sigma_scaling=3, float epsilon=1e-1, int max_iter=20, str image_type="ground", bool positive=True) |
np.ndarray lsst.scarlet.lite.wavelet.apply_wavelet_denoising | ( | np.ndarray | image, |
float | None | sigma = None, | ||
float | sigma_scaling = 3, | ||
float | epsilon = 1e-1, | ||
int | max_iter = 20, | ||
str | image_type = "ground", | ||
bool | positive = True ) |
Apply wavelet denoising Uses the algorithm and notation from Starck et al. 2011, section 4.1 Parameters ---------- image: The image to denoise sigma: The standard deviation of the image sigma_scaling: The threshold in units of sigma to declare a coefficient significant epsilon: Convergence criteria for determining the support max_iter: The maximum number of iterations. This applies to both finding the support and the denoising loop. image_type: The type of image that is being used. This should be "ground" for ground based images with wide PSFs or "space" for images from space-based telescopes with a narrow PSF. positive: Whether or not the expected result should be positive Returns ------- result: The resulting denoised image after `max_iter` iterations.
Definition at line 328 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.bspline_convolve | ( | np.ndarray | image, |
int | scale ) |
Convolve an image with a bspline at a given scale. This uses the spline `h1d = np.array([1.0 / 16, 1.0 / 4, 3.0 / 8, 1.0 / 4, 1.0 / 16])` from Starck et al. 2011. Parameters ---------- image: The 2D image or wavelet coefficients to convolve. scale: The wavelet scale for the convolution. This sets the spacing between adjacent pixels with the spline. Returns ------- result: The result of convolving the `image` with the spline.
Definition at line 35 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.get_multiresolution_support | ( | np.ndarray | image, |
np.ndarray | starlets, | ||
float | sigma, | ||
float | sigma_scaling = 3, | ||
float | epsilon = 1e-1, | ||
int | max_iter = 20, | ||
str | image_type = "ground" ) |
Calculate the multi-resolution support for a dictionary of starlet coefficients. This is different for ground and space based telescopes. For space-based telescopes the procedure in Starck and Murtagh 1998 iteratively calculates the multi-resolution support. For ground based images, where the PSF is much wider and there are no pixels with no signal at all scales, we use a modified method that estimates support at each scale independently. Parameters ---------- image: The image to transform into starlet coefficients. starlets: The starlet dictionary used to reconstruct `image` with dimension (scales+1, Ny, Nx). sigma: The standard deviation of the `image`. sigma_scaling: The multiple of `sigma` to use to calculate significance. Coefficients `w` where `|w| > K*sigma_j`, where `sigma_j` is standard deviation at the jth scale, are considered significant. epsilon: The convergence criteria of the algorithm. Once `|new_sigma_j - sigma_j|/new_sigma_j < epsilon` the algorithm has completed. max_iter: Maximum number of iterations to fit `sigma_j` at each scale. image_type: The type of image that is being used. This should be "ground" for ground based images with wide PSFs or "space" for images from space-based telescopes with a narrow PSF. Returns ------- M: Mask with significant coefficients in `starlets` set to `True`.
Definition at line 236 of file wavelet.py.
int lsst.scarlet.lite.wavelet.get_starlet_scales | ( | Sequence[int] | image_shape, |
int | None | scales = None ) |
Get the number of scales to use in the starlet transform. Parameters ---------- image_shape: The 2D shape of the image that is being transformed scales: The number of scales to transform with starlets. The total dimension of the starlet will have `scales+1` dimensions, since it will also hold the image at all scales higher than `scales`. Returns ------- result: Number of scales, adjusted for the size of the image.
Definition at line 79 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.multiband_starlet_reconstruction | ( | np.ndarray | starlets, |
int | generation = 2, | ||
Callable | None | convolve2d = None ) |
Reconstruct a multiband image. See `starlet_reconstruction` for a description of the remainder of the parameters.
Definition at line 219 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.multiband_starlet_transform | ( | np.ndarray | image, |
int | None | scales = None, | ||
int | generation = 2, | ||
Callable | None | convolve2d = None ) |
Perform a starlet transform of a multiband image. See `starlet_transform` for a description of the parameters.
Definition at line 160 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.starlet_reconstruction | ( | np.ndarray | starlets, |
int | generation = 2, | ||
Callable | None | convolve2d = None ) |
Reconstruct an image from a dictionary of starlets Parameters ---------- starlets: The starlet dictionary used to reconstruct the image with dimension (scales+1, Ny, Nx). generation: The generation of the starlet transform (either ``1`` or ``2``). convolve2d: The filter function to use to convolve the image with starlets in 2D. Returns ------- image: The 2D image reconstructed from the input `starlet`.
Definition at line 182 of file wavelet.py.
np.ndarray lsst.scarlet.lite.wavelet.starlet_transform | ( | np.ndarray | image, |
int | None | scales = None, | ||
int | generation = 2, | ||
Callable | None | convolve2d = None ) |
Perform a starlet transform, or 2nd gen starlet transform. Parameters ---------- image: The image to transform into starlet coefficients. scales: The number of scale to transform with starlets. The total dimension of the starlet will have `scales+1` dimensions, since it will also hold the image at all scales higher than `scales`. generation: The generation of the transform. This must be `1` or `2`. convolve2d: The filter function to use to convolve the image with starlets in 2D. Returns ------- starlet: The starlet dictionary for the input `image`.
Definition at line 104 of file wavelet.py.