Coded-Exposure Imaging for Motion Deblur L1-077
Unclaimed Principle — open for contribution
This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.
To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.
Forward model E
A shutter opens and closes according to a binary temporal code c(t) during a single exposure T, so the camera integral of a moving scene is a convolution by a broadband temporal kernel h_c(t) = c(t) instead of a low-pass box h_box(t) = rect(t/T). The broadband kernel preserves high spatial frequencies of the moving object, making invertible motion deblur possible.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 900
Coded kernel h_c is chosen to have approximately flat magnitude response (MURA or optimal binary codes); OTF is nonzero across full passband, so Wiener deconvolution is well-conditioned. Stability dominated by motion-direction / velocity mismatch and saturation clipping.
Solvability C
- Solver class:
- Wiener deconvolution, blind-deconv (BRISQ-conv, Fergus), learned (DeepDeblur, MPRNet when coded prior known)
- Convergence rate q:
- 2
- Complexity:
- O(H * W * log(H*W)) per iteration (1D FFT along motion axis)