Integrated Forward–Inverse Network for Reconstruction for Lensless Image Reconstruction

Lensless imaging enables compact and versatile computa-tional cameras by replacing bulky optics with thin coded elements. How-ever, reconstruction from the resulting measurements is challenging: large-footprint point-spread functions (PSFs) produce highly multiplexed ob-servations, making inversion severely ill-conditioned and sensitive to cal-ibration errors and model mismatch. While deep learning approaches,including hybrid models that incorporate physics priors, have shownpromise, explicitly maintaining data fidelity throughout the network hier-archy remains difficult. Here, we propose the Integrated Forward–InverseNetwork (IFIN), a physics-guided architecture that interleaves differen-tiable forward projections with learnable inverse updates at every scale,enabling complementary cues to be exploited jointly in the measurementand image domains. This bidirectional coupling supports progressive,physics-consistent refinement and permits system-constrained PSF ker-nel adaptation under model uncertainty. On challenging lensless bench-marks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performanceon Gaussian deblurring and simulated inline holography reconstruction,suggesting that the same interleaving principle can extend beyond lens-less cameras.