Single-frame fringe analysis drops the hand-tuned filter window: spectral attention adapts per image, and simulation-trained models reconstruct unseen experimental data
Single-frame fringe pattern analysis in digital holographic interferometry has relied on manually set Fourier-domain filter windows, which are hard to choose when noise, defocus or overexposure degrade the fringes. An adaptive spectrum filtering network turns Fourier-domain filtering into learnable attention weights that select +1st-order spectral content per input, and is trained on forward-model simulations with added noise and overexposure. On 400 simulated test samples it beats tuned Fourier-transform analysis and an end-to-end ResUNet in pixel error, and after simulation-only training it produces fewer artifacts and outliers than hand-tuned Fourier analysis on unseen experimental interferograms.