Experimental parameter error compensation in deep-learning-based coherent diffraction imaging

The integration of deep learning into computational imaging has driven substantial advancements in coherent diffraction imaging (CDI).While physics-driven neural networks have emerged as a promising approach through their unsupervised learning paradigm, their practical implementation faces critical challenges: measurement uncertainties in physical parameters (e.g., the propagation distance and the size of sample area) severely degrade reconstruction quality.To overcome this limitation, we propose a deep-learning-enabled spatial sample interval optimization framework that synergizes physical models with neural network adaptability.Our method embeds spatial sample intervals as trainable parameters within a PhysenNet architecture coupled with Fresnel diffraction physics, enabling simultaneous image reconstruction and system parameter calibration.Experimental validation demonstrates robust performance with structural similarity (SSIM) values consistently maintained at 0.6 across diffraction distances spanning of 10-200 mm, using a 1024 1024 region of interest (ROI) from a 1624 1440 CCD (pixel size: 4.5 m) under 632.8 nm illumination.This framework has excellent fault tolerance, that is, it can still maintain high-quality image restoration even when the propagation distance measurement error is large.Compared to conventional iterative reconstruction algorithms, this approach can transform fixed parameters into learnable parameters, making almost all image restoration experiments easier to implement, enhancing system robustness against experimental uncertainties.This work establishes, to our knowledge, a new paradigm for adaptive diffraction imaging systems capable of operating in complex real scenarios.