arXiv subjects

Ary Portes

Publications and source records attributed to Ary Portes.

3 recordsLinked to original sources

arXiv2026

Learned optical compression for single-pixel quantitative phase imaging

Quantitative phase imaging (QPI) reveals optical path-length variations in transparent objects but typically relies on spatially resolved intensity measurements. Here we numerically and experimentally demonstrate learned optical compression for single-pixel QPI, jointly optimizing a diffractive encoder and a digital neural decoder to concentrate reconstruction-relevant phase information into a compact measurement vector. A single reconfigurable phase-modulation plane sequentially displays the learned encoding patterns, which are independent of the input object, each yielding one scalar intensity measurement. Operating at a visible wavelength, the experimental system reconstructs quantitative phase images of unknown objects with ~40-fold dimensional compression, using only 2.55% as many intensity measurements as reconstructed pixels. Experimental blind testing on handwritten digits and grating-like phase objects achieves mean Pearson correlation coefficients of 0.945 and 0.848, respectively. Benchmarking under the same measurement budget demonstrates substantially higher reconstruction fidelity than other encoding strategies, highlighting the advantages of learned optical compression. With the optical encoder fixed, decoder-only neural adaptation enables experimental transfer to previously unseen data distributions, extending the utility of the learned measurement representation. These results establish learned optical compression as a measurement-efficient and reusable framework for QPI, integrating phase-information compression into single-pixel intensity measurements while retaining adaptability to new data. This approach paves the way for low-data-rate phase imaging and sensing, with the potential to extend QPI to spectral regions where high-quality detector arrays are costly or unavailable.

physics.optics↗

arXiv2026

Phase-aware Inverse Design for Silicon Photonic Logic Gates

Inverse design has emerged as a powerful strategy for realizing compact photonic devices; however, its application to logic operations remains constrained by challenges in physical interpretability, architectural generality, and experimental validation. This work introduces an experimentally validated, unified, and physics-driven inverse design framework that implements all fundamental Boolean logic gates within a single silicon photonic architecture. Devices are fabricated within a 2 x 2 um^2 design region on a silicon-on-insulator platform, representing one of the smallest areas reported for photonic logic elements. By integrating amplitude, phase, and energy conservation into a composite figure of merit, the proposed approach enables direct control over constructive and destructive interference. Consequently, all logic functions, including XOR and three-input NAND/NOR operations, are achieved using a standardized configuration with two logical inputs and a bias port. Experimental results exhibit good agreement with numerical simulations across the C-band, confirming both the predictive accuracy and fabrication robustness of the method. Performance benchmarking reveals competitive contrast ratios compared to previous implementations, while providing a unified and scalable design strategy. These findings establish a physically interpretable and experimentally validated paradigm for inverse-designed photonic logic, advancing the development of compact, integrated optical computing systems.

physics.optics↗

arXiv2025

Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning

Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a stricter resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIM_PSR_k, a deep learning-based multi-channel pixel super-resolution (PSR) framework that reconstructs high-resolution FLIM images from data acquired with up to a 5-fold increased pixel size. The model is trained using the conditional generative adversarial network (cGAN) framework, which, compared to diffusion model-based alternatives, delivers a more robust PSR reconstruction with substantially shorter inference times, a crucial advantage for practical deployment. FLIM_PSR_k not only enables faster image acquisition but can also alleviate SNR limitations in autofluorescence-based FLIM. Blind testing on held-out patient-derived tumor tissue samples demonstrates that FLIM_PSR_k reliably achieves a super-resolution factor of k = 5, resulting in a 25-fold increase in the space-bandwidth product of the output images and revealing fine architectural features lost in lower-resolution inputs, with statistically significant improvements across various image quality metrics. By increasing FLIM's effective spatial resolution, FLIM_PSR_k advances lifetime imaging toward faster, higher-resolution, and hardware-flexible implementations compatible with low-numerical-aperture and miniaturized platforms, better positioning FLIM for translational applications.

cs.CV↗