arXiv subjects

Steve F. Shu

Publications and source records attributed to Steve F. Shu.

3 recordsLinked to original sources

arXiv2026

High-fidelity tabletop nanoscopy enabled by non-linear spectral preconditioning

Ptychography is a powerful lensless imaging technique that overcomes conventional numerical aperture limits to achieve diffraction-limited resolution. While routine at high-brilliance synchrotron facilities, its application to laboratory-scale sources is primarily limited by low photon flux. Under these conditions, the wide dynamic range of diffraction signals presents a critical bottleneck where detector bit-depth limitations hinder the simultaneous recording of low-frequency intensity and high-frequency details. Currently, most high-dynamic-range (HDR) imaging methods enforce strict radiometric linearity, assuming the fused intensity must be linearly proportional to the squared modulus of the wavefront to satisfy Poisson likelihood models. In this paper, we introduce a multi-scale non-linear fusion approach into the ptychographic pipeline, demonstrating that strict linearity is not a prerequisite for accurate reconstruction. This method mitigates the traditional trade-off between noise suppression and physical fidelity, enables robust imaging under strong dispersion, and significantly broadens the effective spectral bandwidth.

cs.GR↗

arXiv2026

Breaking optoelectronic SNR limitations via physics-consistent computational diffractive imaging

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.

cs.GR↗

arXiv2026

Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction

Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean scalar despite its $2π$ periodicity, which can introduce wrapping artifacts, discontinuities at $\pmπ$, and a mismatch between the loss and the underlying signal geometry. We present a deep learning framework for ptychographic reconstruction that models phase on the unit circle using cosine and sine components. Phase error is optimized with a differentiable geodesic loss, which avoids branch-cut discontinuities and provides bounded gradients. The network further incorporates saturation-aware dual-gain input scaling, parallel encoder branches, and three decoders for amplitude, cosine, and sine prediction, together with a composite loss that promotes circular consistency and structural fidelity. Experiments on synthetic and experimental datasets show consistent improvements in both amplitude and phase reconstruction over existing deep learning methods. Frequency-domain analysis further shows better preservation of mid- and high-frequency phase content. The proposed method also provides substantial speedup over iterative solvers while maintaining physically consistent reconstructions.

eess.IV↗