Color-resolved light field shaping via diffractive-electronic U-shape network with wavelength-aware virtual branching.

Abstract

Dynamic spatial distortions in coherent light beams present a major challenge for stable and high-fidelity optical field shaping, particularly when the target output is a color-resolved pattern. Existing beam shaping techniques, including recent diffractive optical neural networks, are typically limited to monochromatic or grayscale targets and struggle to generalize across temporally varying, multi-distorted inputs. In this work, we propose a diffractive-electronic hybrid neural network tailored for real-time, color light field shaping. To improve spectral generalization, we introduce a wavelength-aware virtual branching (WAVB) mechanism during training, enabling the network to adaptively learn wavelength-specific shaping strategies without modifying the physical design. On the electronic side, we integrate a spectrally conditioned U-shape network, which is structurally adapted to preserve inter-channel dependencies. We implement frequency-selective skip connections (FSSC), allowing the network to emphasize mid- and high-frequency feature restoration while avoiding overcompensation in low-frequency regions. Additionally, we introduce an all-optical-driven optical flow prediction module, enabling frame-to-frame tracking and reverse inference of the beam's evolution, thus enhancing temporal coherence. Our system achieves real-time operation at 50Hz, delivering robust, frame-stable color light field shaping across a range of spatial and temporal distortion scenarios. This work provides a task-specific, scalable framework for intelligent, adaptive imaging systems, with promising applications in dynamic holography, laser-based displays, and computational optical imaging.