Does modelling the sub-pixel coverage of a moving subject fix the edge ring that burst merging leaves behind?

What it found

Follow-up to sensing-ghostring, where no merge or mask fixed the edge ring. Here each frame is a layered mix, a·disc + (1−a)·background, with sub-pixel coverage a from a fitted disc, and the background solved by least squares over the frames. On the same synthetic bursts at 8 frames and 2 px per frame the ring goes from 27.1 dB (aligned plus reject) to 33.1 dB with the disc fitted from the burst itself (large sensor, 4× area: 31.7 dB); at 0.5–1 px per frame it gains 0.4–2.2 dB and stays below the large frame. It is sensitive to geometry: a 0.5 px centre error drops the ring to 25 dB. Stylised: the model assumes the exact shape of the synthetic subject (a flat disc), so this is an upper-bound-style result, not a general deghosting method.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Sensing, imaging and 3D

• Can a background estimate or a subject mask fix the edge ring that burst alignment leaves behind?Can a temporal-median background or a subject mask fix the edge ring that burst alignment leaves behind? No: four ring treatments leave it at 21.6–27.2 dB at 8 frames against 31.6–32.0 for a 4×-area frame, none beats plain aligned-plus-reject (27.0 dB), and the mask misses most of a slow subject. Synthetic scenes only.
• How accurately does a real keypoint detector localise features as images get noisier and blurrier?On 30 warped real photographs SIFT keypoints localise to 0.13 px per axis at zero noise and 0.32 px at noise sigma 10 grey levels, 0.59 px at 40, so sensing-ba's assumed 0.5 px sits at roughly sigma 28; large keypoints are worse, blur matters little
• Does a gate or a RANSAC start survive the mismatch mixes a real SIFT matcher produces?With the mismatch rates and along/near/far mixes measured for SIFT, a ratio test of 0.8 or tighter (10% wrong or fewer) leaves gate and RANSAC-start adjustment near the clean floor (0.002 to 0.02 radii) while an all-view start is 0.005 to 0.39; at ratio 0.9 the RANSAC start wins and with no ratio test (56% wrong) everything fails. Synthetic rig, measured mixes
• What do a real matcher's mismatches look like: random pixels, near misses, or along the epipolar line?On a real Middlebury stereo pair SIFT with no ratio test is 56% wrong and 84% of the wrong ones are far, but at a 0.7 ratio test 6% are wrong and 62% of those lie on the true epipolar line (chance: 1%); ten warped photographs give 40% wrong without and 2% with ratio 0.6
• Does an epipolar gate help bundle adjustment under the mismatch mix a real SIFT matcher produces?Feeding sensing-matchreal's measured SIFT mismatch mixes into the sensing-epigate trial, gate plus Cauchy beats a RANSAC start at ratio tests of 0.8 and tighter (0.0078 against 0.0207 object radii at 0.8) and loses only at a loose 0.9 ratio test, where all methods exceed 0.08; synthetic geometry, 6 seeds
• Is an INS/GNSS Kalman filter honest about its drift when accelerometer bias is flicker noise?With a 1/f accelerometer bias, a filter with no bias state claims 2.9 m after a 300 s GNSS outage when the RMS is 151 m (NEES/dof 514 to 2,000 at 100 ug); a random walk read off the Allan curve at 1 s or 10 s claims 2.4 to 3.7 times the true error, and at 100 s close to honest (0.9 to 1.6). Synthetic bias, swept instability

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.