Stimulated Emission Depletion (STED) — donut super-resolution L1-010
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
y(r) = [PSF_sted(r; P_depl) conv f(r)] + n; effective FWHM = FWHM_0 / \sqrt{1 + P_depl/P_sat)Stimulated Emission Depletion (STED) — donut super-resolution: sted donut depletion produces the measurement through a 3-node primitive DAG L.excitation.focused -> L.depletion.donut -> int.temporal, with time-integrated exposure and photon-shot-noise-limited (Poisson counting). Recovery is posed as a linear inverse problem that inverts the forward operator to estimate the scene-side 2D intensity. Difficulty tier delta=5 with effective condition number kappa_eff~20; calibration-level mismatch (donut_zero_depth, depletion_power_drift, anti_stokes_leakage) 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:
- 400
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 moderately conditioned (kappa_eff ~= 20); donut_zero_depth dominates the stability cliff; depletion_power_drift and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) sets the irreducible data-fidelity floor, while TV / wavelet-sparsity / deep priors stabilise recovery at the ill-conditioned end of Omega.
Solvability C
- Solver class:
- iterative maximum-likelihood (Richardson-Lucy class) [Richardson-Lucy-STED] | linear-operator + convex optimisation [TV-ADMM-STED] | linear-operator + deep neural prior [STED-Net]
- Convergence rate q:
- 2
- Complexity:
- O(H * W * log(...)) per iteration; learned variants: O(H W Z * F_theta_cost) per forward pass