Breaking optoelectronic SNR limitations via physics-consistent computational diffractive imaging

2026-08-03

AI Summary

This work addresses the degradation of high-frequency signal fidelity, limited signal-to-noise ratio (SNR), and resolution in conventional ptychographic imaging under photon-limited conditions, which arises from spatially heterogeneous detector noise. To overcome this, the authors propose integrating pixel-level detector response calibration directly into the reconstruction pipeline, generating a spatially resolved confidence map that is embedded as an inner-loop physical constraint during iterative amplitude updates. This enables adaptive weighting of diffraction data, dynamically suppressing unreliable residuals while preserving physically meaningful information. By relaxing the ideal-detector assumption inherent in traditional algorithms, the method achieves approximately twofold SNR improvement across transmission, reflection, and weak-phase biological imaging modalities, with reconstructed resolution approaching the Rayleigh limit (k ≈ 0.65). The approach significantly outperforms existing denoising strategies by striking a superior balance between noise suppression and structural fidelity.

Abstract

Ptychography is a powerful lensless imaging technique capable of approaching the diffraction limit, yet its performance is increasingly constrained by non-ideal detection hardware. In photon-limited measurements, weak high-frequency diffraction signals often overlap with spatially heterogeneous detector noise, whereas most reconstruction algorithms still treat the detector as an ideal measurement plane. Here, we introduce detector-informed measurement consistency into ptychographic reconstruction. By calibrating the pixelwise sensor response, the method construct a spatially resolved confidence map and embed it into the iterative amplitude constraint, allowing unreliable detector residuals to be down-weighted while preserving physically meaningful diffraction information. Experiments across transmission, reflection, and weak biological phase imaging show improved diffraction-data quality, an approximately twofold signal-to-noise ratio (SNR) enhancement, and reconstruction approaching the Rayleigh limit with a measured (k)-factor of about 0.65. Compared with previous advanced denoising methods, the proposed framework achieves a better balance between suppressing detector-induced background and preserving structural diffraction information. These results show that detector reliability can be used as an in-loop physical constraint to extend the performance of ptychographic imaging with imperfect sensors.

Problem

• ptychography
• detector noise
• signal-to-noise ratio
• diffraction imaging
• non-ideal sensors

Innovation

• detector-informed reconstruction
• ptychography
• computational diffractive imaging
• spatially resolved confidence map
• SNR enhancement