typed op• Data kinds: counts → counts
• Call: fullseye.apply(img, "tb_tcspc_background_subtract", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_tcspc_background_subtract: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_tcspc_background_subtract: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_tcspc_background_subtract: other inputs (docs site)
Remove the ambient-light / dark-count floor from an arrival-time histogram.
Outdoors, most of what a dToF sensor counts is sunlight: a roughly uniform
pedestal under the return pulse. It biases the centroid toward the middle of
the window (a floor of `b` per bin pulls the first moment toward
`window/2`) and it inflates the apparent signal, so it is removed before
any depth or lifetime estimate.
The level is estimated by *method* and then subtracted (the sign trap:
the result is `hist - level, clipped at 0, never hist + level`):
• `"median"` (default) — the median of every bin. Robust while the pulse
occupies well under half the window, which is the normal dToF case.
• `"leading"` — the mean of the first *leading_bins* bins, the classical
choice when the pulse is known to arrive late (a far target).
• `"trailing"` — the mean of the last *leading_bins* bins, for
fluorescence decays where the tail is background.
• `"quantile"` — the given *quantile* of all bins, for tuning by hand.
*leading_bins* defaults to `None = min(8, len(hist))`, so the default
call works on a short histogram instead of raising over a constant nobody
chose (a fixed default of 8 made `method="leading"` fail on any histogram
with fewer than 8 bins).
*scale* multiplies the estimated level before subtraction (`scale=1.2` for
a deliberately aggressive removal). Clipping at 0 means the result is a valid
non-negative histogram that the rest of this module will accept.
Ground truth: on a noiseless histogram with a known flat pedestal of 20
counts/bin under a 5000-photon pulse covering 5.1% of the window, the median
estimate recovers 20.000000 and the returned histogram equals the pedestal-
free pulse exactly (measured area error 0.0, pinned in the tests).
Returns a float64 1-D histogram of the same length as *hist*.
Raises `ValueError`: negative, non-finite or non-1-D *hist*, an unknown
*method*, a *leading_bins* outside `[1, len(hist)]`, a *quantile* outside
`[0, 1]`, and a negative *scale*.
Typed bridge of the photon op `tcspc_background_subtract into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives quantile (default 0.5) and b drives scale` (default 1).
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_counts 0.50 0.50 tb_tcspc_background_subtract 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op tcspc_background_subtract. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• photon_timeresolved — py -3.11 examples/photon_timeresolved.py
• poc_dtof_ranging — py -3.11 examples/poc_dtof_ranging.py
counts as input)identity · tb_spad_deadtime_apply · tb_spad_deadtime_correct · tb_tcspc_coates_correct · tb_tcspc_irf_convolve · tb_dtof_depth · tb_countrate_to_counts · tb_counts_to_countrate
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.