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Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging

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DataONE2025-11-24 更新2025-12-06 收录
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Inspired by quantum nonlocal aberration cancellation, the method proposes a computational wavefront shaping (CWS) approach to address the heavy hardware demands of correcting complex aberrations in optical imaging. By exploiting classical correlated light, CWS digitally corrects aberrations on the signal path by introducing a virtual wavefront corrector during the computational propagation of a reference field, entirely bypassing the need for physical corrective elements. The optimal correction is determined by optimizing a image sharpness metric of the computationally reconstructed image, rather than using physical wavefront sensors or interferometric detection. This closed-loop process—encompassing aberration characterization, wavefront correction, and image reconstruction—is performed computationally using only a single pixel detector, thereby significantly relaxing hardware requirements. Experimental results (In this dataset ZIP file) confirmed that CWS effectively restores image qu..., , # Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging [Dryad Link](https://doi.org/10.5061/dryad.5tb2rbph4) ## Brief summary This repository contains code for “Quantum-inspired computational wavefront shaping enables turbulence-resilient distributed aperture synthesis imaging”. The optimization is guided by the image sharpness metric through a PyTorch-based optimizer (Adam) and optional GPU acceleration. The code can reconstruct two example datasets corresponding to `fig3` and `fig4` in the original analysis. ## Description of the data and file structure Top-level files and folders in QiCWS_code_data.zip are listed as following: - `opt.py`: main Python script implementing reconstruction and optimization using PyTorch. - `fig3/`, `fig4/`: dataset folders. Each dataset folder contains `bktsequence.mat`, `phir_array.mat`, and a `mask.mat` file containing the sampling mask which is loaded on the spatial light modulator. ...,
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2025-11-25
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