Lensless imaging enables compact and versatile computational cameras by replacing bulky optics with thin coded elements. However, reconstruction from the resulting measurements is challenging: large-footprint point-spread functions (PSFs) produce highly multiplexed observations, making inversion severely ill-conditioned and sensitive to calibration errors and model mismatch. While deep learning approaches, including hybrid models that incorporate physics priors, have shown promise, explicitly maintaining data fidelity throughout the network hierarchy remains difficult. Here, we propose the Integrated Forward-Inverse Network (IFIN), a physics-guided architecture that interleaves differentiable forward projections with learnable inverse updates at every scale, enabling complementary cues to be exploited jointly in the measurement and image domains. This bidirectional coupling supports progressive, physics-consistent refinement and permits system-constrained PSF kernel adaptation under model uncertainty. On challenging lensless benchmarks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performance on Gaussian deblurring and simulated inline holography reconstruction, suggesting that the same interleaving principle can extend beyond lensless cameras.
Donggeon Bae, Jaewoo Jung, Y. Kang et al.· 0 citations
Laser speckle imaging is essential for continuous hemodynamic monitoring, but emerging high-speed modalities such as rolling shutter speckle imaging (RSSI) are severely bottlenecked by intensive computational demands and high susceptibility to noise caused by statistical undersampling. To address these fundamental limitations, we introduce the Bifurcated Speckle Network (BiSNet), a hybrid deep learning architecture that merges empirical data training with analytical physical models to mitigate noise artifacts of undersampled speckle measurements in RSSI. To realize this hybrid training framework, BiSNet links localized architectural constraints with physics-guided optimization. By bounding the network's receptive field, the model is forced to evaluate flow dynamics based on localized speckle fluctuations. This prevents the network from memorizing the macroscopic, uniform structures of physical phantoms. Also, a physics-guided loss embeds the RSSI forward model directly into the training process, enabling the network to learn the governing physics. This approach improves data efficiency, allowing the model to generalize across diverse flow conditions without requiring an exhaustive library of experimental phantom datasets. Validated using controlled microfluidic setups and in vivo cranial window models, BiSNet delivers high-fidelity, real-time hemodynamic mapping. By effectively suppressing undersampling noise and bypassing the latency of traditional iterative solvers, our proposed framework accelerates accurate flow parameter estimation for continuous biological monitoring.
Sangjun Byun, Changyoon Yi, Donggeon Bae et al.· Journal of Physics: Photonic...· 0 citations