Image recovery improves using neural priors with Fourier phase data
Image Reconstruction from Phase with Untrained Neural Priors
Computer Vision and Pattern Recognition
Summary
Recovering an image when you only have its phase information is tricky because some details and brightness can't be determined directly. The paper’s authors created a two-step way to reconstruct images by combining known image shapes and a special, flexible neural network that adapts to each image. They tested their method on microscopy pictures and found it produced clearer and more accurate images than simple methods. However, they also found that just reducing phase errors doesn't always mean better final images.
What this means in practice
- •For microscopy imaging teams: Produce higher-quality images from limited Fourier phase data using neural-guided reconstruction techniques.
- •For medical image analysts: Improve image recovery where spectral magnitude information is unavailable, supporting better diagnostics from partial data.
Authors
Ene Meco, Ahmet Enis Cetin
Abstract
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 variant achieves 31.41 dB pooled PSNR, 35.75 dB mean PSNR, and 0.9531 mean SSIM, improving pooled PSNR by 1.51~dB and reducing pooled MSE by 29.3% relative to the baseline. The results demonstrate the benefit of combining neural guidance with explicit constraint refinement at the evaluated iteration budget, while showing that lower phase residual alone does not guarantee greater reconstruction accuracy.