Enhancing lensless imaging via Explicit Learning of Model Mismatch
Keywords: Lensless Imaging; Maximum a Posteriori;Model Mismatch Error;Image Reconstruction
Abstract: Emerging lensless imaging techniques hold promise for miniaturized cameras, but their effectiveness is constrained by challenges like model mismatch from the point spread function (PSF), which undermines reconstruction methods dependent on accurate PSF modeling. To address this issue, we propose a joint Maximum a Posteriori (MAP) approach to simultaneously estimate model mismatch error (${\rm M^{2}}$E) and reconstruct high-resolution images from lensless imaging measurements. Specifically, we propose an explicit latent space representation for ${\rm M^{2}}$E to improve robustness against PSF inaccuracies. Additionally, we develop a multi-stage reconstruction network by unfolding the joint MAP estimator with a learned Laplacian Scale Mixture (LSM) prior and ${\rm M^{2}}$E representation (${\rm M^{2}}$ER) through end-to-end optimization. Extensive experiments show that our method surpasses current state-of-the-art methods.
Primary Area: interpretability and explainable AI
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Submission Number: 6256
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