mlsanityData to 7 Oct 2026

Prior Image-Constrained Reconstruction using Style-Based Generative Models

arXiv 2102.12525 ↗First posted 24 Feb 2021eess.IV cs.CV cs.LGcited by 19

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Abstract (arXiv)

Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning object priors or constraints to improve the conditioning of an ill-posed imaging inverse problem. In this study, a framework for estimating an object of interest that is semantically related to a known prior image, is proposed. An optimization problem is formulated in the disentangled latent space of a style-based generative model, and semantically meaningful constraints are imposed using the disentangled latent representation of the prior image. Stable recovery from incomplete measurements with the help of a prior image is theoretically analyzed. Numerical experiments demonstrating the superior performance of our approach as compared to related methods are presented.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 24 Feb 2021.

Builds on

21 of 40 references.

References · 21

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9 more
  1. 2020On hallucinations in tomographic image reconstructioncited by 66
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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 3; 2025: 3.

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  1. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  2. 2023MRI Reconstruction with Side Information using Diffusion Modelscited by 15
  3. 2021Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networkscited by 12
  4. 2022Assessing the ability of generative adversarial networks to learn canonical medical image statisticscited by 11
  5. 2022Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problemscited by 11
  6. 2023AmbientFlow: Invertible generative models from incomplete, noisy measurementscited by 11
  7. 2025A Trust-Guided Approach to MR Image Reconstruction with Side Informationcited by 6
  8. 2021Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Modelscited by 3
  9. 2022Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problemscited by 3
  10. 2023Deep Injective Prior for Inverse Scatteringcited by 3
  11. 2023High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Modelscited by 3
  12. 2024Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Modelscited by 3
7 more
  1. 2022Prior image-based medical image reconstruction using a style-based generative adversarial networkcited by 2
  2. 2022Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imagingcited by 1
  3. 2024Latent Space Imagingcited by 1
  4. 2025Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosiscited by 1
  5. 2023Conditioning Generative Latent Optimization for Sparse-View CT Image Reconstructioncited by 0
  6. 2024Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstructioncited by 0
  7. 2025An Adaptive, Disentangled Representation for Multidimensional MRI Reconstructioncited by 0