Learning Adaptive Filtering in the Fourier Domain for Fringe Pattern Analysis: A New Reconstruction Paradigm in...

Single-frame fringe pattern analysis in digital holographic interferometry (DHI) is essential for precision phase measurement under dynamic conditions. Recent advances in deep learning within optical metrology are transforming the reconstruction paradigm from physics-based modeling toward data-driven learning. However, most existing neural networks either rely on learning statistical visual features without guidance from physical principles or fail to dynamically adjust inference parameters based on the input instance after training, thereby limiting the robustness and generalization capacity on unseen data. To this end, a novel adaptive spectrum filtering neural network (ASFNet) is proposed for phase measurement. ASFNet incorporates a physics-aware spectral filtering block that dynamically adapts the inference process to each input instance, enabling robust and generalizable phase reconstruction under varying and unseen conditions-moving beyond static, dataset-specific statistical modeling toward instance-aware adaptive reconstruction. Moreover, a forward model is constructed to generate realistic simulation data for network training, reducing the reliance on experimentally labeled data. Simulation results on 400 test samples demonstrate the superiority of ASFNet in terms of both pixel-wise accuracy and visual quality. Experimental results further confirm its strong generalization capability on unseen data, achieving high reconstruction fidelity comparable to that of hand-tuned, physics-based methods.