Image Reconstruction from Phase with Untrained Neural Priors
Fourier phase encodes important spatial image structure, but recovering an image without measured spectral magnitude requires additional constraints and leaves absolute intensity ambiguous. We propose a projection-based two-stage framework that combines Fourier-phase and spatial-support constraints with an image-specific neural prior. The first stage alternates constraint enforcement with regularized neural-prior updates, while the second performs phase/support refinement alone with guaranteed convergence. We evaluate two neural-prior implementations on the same 77 microscopy images and compare them with a constraint-only baseline. After 500 final refinement passes, the best-performing va...
| Authors | Ene Meco, Ahmet Enis Cetin |
|---|---|
| Primary category | eess.IV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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