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

• URL: http://arxiv.org/abs/2604.26664v1
• Date: Wed, 29 Apr 2026 13:27:05 GMT
• Title: Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction
• Abstract summary: We present a deep learning framework for ptychographic reconstruction that models phase on the unit circle using cosine and sine components.<n>Phase error is optimized with a differentiable geodesic loss, which avoids branch-cut discontinuities.<n> Experiments on synthetic and experimental datasets show consistent improvements in both amplitude and phase reconstruction over existing deep learning methods.
• Score: 6.504427780020361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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