Learning Physics-based Forward Model Corrections in Unrolled Networks for Diffuser-based Imaging

Diffuser-based imaging systems replace conventional lenses with optical diffusers to enable compact lensless cameras, but their performance critically depends on computational reconstruction from compressed measurements. Deep unrolled networks have emerged as a leading framework by incorporating physical forward models into iterative optimization. However, existing methods typically rely on simplified assumptions, most notably shift-invariant point spread functions (PSFs), which fail to capture spatially-varying aberrations and geometric distortions in real hardware. In this paper, we propose learning physics-based forward model corrections for diffuser-based imaging. We construct a physically interpretable forward model with learnable correction terms based on Eigen-Kernel decomposition to handle spatially-varying aberrations. This design preserves the structure of the physical model while improving its fidelity to real measurements. The corrected forward model is integrated into an unrolled reconstruction framework (EK-DUN), leading to consistent improvements in reconstruction accuracy. Experimental results on real-world data demonstrate that our method achieves stateof-the-art performance compared to existing unrolled baselines.