Lensless Imaging with Diffuser PSF (DiffuserCam) L1-075
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
DiffuserCam replaces the lens with a random phase mask (diffuser) that produces a spatially-varying caustic PSF. Each scene point maps to a large, high-contrast caustic pattern on the sensor; under shift-invariance the full sensor image is the convolution of the scene with the calibrated diffuser PSF, with sensor crop C applied at the boundary.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 1800
Deconvolution with a wideband random PSF is well-conditioned over the non-zero OTF support (caustics have rich high-frequency content). Cropping by sensor extent introduces boundary ill-posedness (recoverable via TV + boundary-extension priors). Mismatch between calibrated PSF and actual PSF (temperature drift, mask rotation) dominates stability.
Solvability C
- Solver class:
- FISTA-TV, ADMM with Anderson acceleration, Le-ADMM-U (learned unrolling), Plug-and-Play with deep denoisers
- Convergence rate q:
- 2
- Complexity:
- O(H' * W' * log(H'*W')) per iteration (FFT-based convolution)