LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction
Journal:
arXiv
Published Date:
May 3, 2025
Abstract
Lensless imaging stands out as a promising alternative to conventional
lens-based systems, particularly in scenarios demanding ultracompact form
factors and cost-effective architectures. However, such systems are
fundamentally governed by the Point Spread Function (PSF), which dictates how a
point source contributes to the final captured signal. Traditional lensless
techniques often require explicit calibrations and extensive pre-processing,
relying on static or approximate PSF models. These rigid strategies can result
in limited adaptability to real-world challenges, including noise, system
imperfections, and dynamic scene variations, thus impeding high-fidelity
reconstruction. In this paper, we propose LensNet, an end-to-end deep learning
framework that integrates spatial-domain and frequency-domain representations
in a unified pipeline. Central to our approach is a learnable Coded Mask
Simulator (CMS) that enables dynamic, data-driven estimation of the PSF during
training, effectively mitigating the shortcomings of fixed or sparsely
calibrated kernels. By embedding a Wiener filtering component, LensNet refines
global structure and restores fine-scale details, thus alleviating the
dependency on multiple handcrafted pre-processing steps. Extensive experiments
demonstrate LensNet's robust performance and superior reconstruction quality
compared to state-of-the-art methods, particularly in preserving high-frequency
details and attenuating noise. The proposed framework establishes a novel
convergence between physics-based modeling and data-driven learning, paving the
way for more accurate, flexible, and practical lensless imaging solutions for
applications ranging from miniature sensors to medical diagnostics. The link of
code is https://github.com/baijiesong/Lensnet.
