Low-Dose Widefield Fluorescence (photon-starved Airy deconvolution) L1-002
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
Low-Dose Widefield Fluorescence (photon-starved Airy deconvolution): fluorescence widefield produces the measurement through a 2-node primitive DAG K.psf.airy -> int.temporal, with time-integrated exposure and Poisson signal noise + Gaussian read noise. Recovery is posed as a linear inverse problem that inverts the forward operator to estimate the scene-side 2D intensity. Difficulty tier delta=3 with effective condition number kappa_eff~8; calibration-level mismatch (dz_nm, sigma_bg, shot_noise_variance) sets the accuracy floor at the Omega boundary. See the forward_model field for the closed-form imaging equation.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 160
Existence of the recovered 2D intensity is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is well-conditioned (kappa_eff ~= 8); dz_nm dominates the stability cliff; sigma_bg and the remaining mismatch parameters contribute higher-order bias terms. Poisson signal noise + gaussian read noise sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.
Solvability C
- Solver class:
- linear-operator + analytic regularisation [Anscombe-BM3D] | linear-operator + deep neural prior [CARE-UNet-Poisson, Noise2Noise]
- Convergence rate q:
- 2
- Complexity:
- O(H * W * log(...)) per iteration; learned variants: O(H W Z * F_theta_cost) per forward pass