Parallel consensus optimization for high-accuracy and robust lensless imaging

Le résumé fourni par la source

Lensless imaging is attractive for its compact and low-cost design. However, high-accuracy and robust phase retrieval from a single pattern remains challenging, critically relying on accurate priors. Here, we propose parallel consensus optimization termed PaCO, to achieve robust and high-accuracy lensless imaging. PaCO decomposes the ill-posed problem into parallel subproblems with a distributed computational architecture. Each subproblem is handled by dedicated solvers, respectively enforcing data fidelity, Fourier support, and spatial smoothness. The local solutions are aggregated into a consensus solution. Experiments on a mask-based system demonstrate that PaCO can reconstruct objects without precise support and distance priors and accurately retrieves vortex and binary-phase plate wavefronts, where conventional methods suffer from reconstruction instability. Statistical tests confirm PaCO improves the success rate from 74% to 96.8% while halving the processing time. The parallel architecture of PaCO is inherently CUDA-friendly, holding strong potential for real-time processing in extreme-scale imaging workflows.

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