Image Cleaning

Cleaning/denoising of images (tailcuts cleaning, dilation, filtering).

An example of image cleaning and dilation:

API Reference

ctapipe.image.cleaning Module

Image Cleaning Algorithms (identification of noisy pixels)

All algorithms return a boolean mask that is True for pixels surviving the cleaning.

To get a zero-suppressed image and pixel list, use image[mask], geom.pix_id[mask], or to keep the same image size and just set unclean pixels to 0 or similar, use image[~mask] = 0

Functions

tailcuts_clean(geom, image[, ...])	Clean an image by selection pixels that pass a two-threshold tail-cuts procedure.
dilate(geom, mask)	Add one row of neighbors to the True values of a pixel mask and return the new mask.
mars_cleaning_1st_pass(geom, image[, ...])	Clean an image by selection pixels that pass a three-threshold tail-cuts procedure.
fact_image_cleaning(geom, image, arrival_times)	Clean an image by selection pixels that pass the fact cleaning procedure.
apply_time_delta_cleaning(geom, mask, ...)	Identify all pixels from selection that have less than N neighbors that arrived within a given timeframe.
apply_time_average_cleaning(geom, mask, ...)	Extract all pixels that arrived within a given timeframe with respect to the time average of the pixels on the main island.
time_constrained_clean(geom, image, ...[, ...])	time constrained cleaning by MAGIC

Classes

ImageCleaner(**kwargs)	Abstract class for all configurable Image Cleaning algorithms.
TailcutsImageCleaner(**kwargs)	Clean images using the standard picture/boundary technique.

Class Inheritance Diagram