# Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction

arXiv: https://arxiv.org/abs/2007.02462 · HTML: https://mlsanity.com/paper/2007.02462/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 19 of 19)

- [Robust Compressed Sensing MRI with Deep Generative Priors](https://mlsanity.com/paper/2108.01368/) (2021)
- [Prior Image-Constrained Reconstruction using Style-Based Generative Models](https://mlsanity.com/paper/2102.12525/) (2021)
- [One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction](https://mlsanity.com/paper/2208.07181/) (2022)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [AmbientFlow: Invertible generative models from incomplete, noisy measurements](https://mlsanity.com/paper/2309.04856/) (2023)
- [WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction](https://mlsanity.com/paper/2205.03883/) (2022)
- [K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction](https://mlsanity.com/paper/2203.10776/) (2022)
- [High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models](https://mlsanity.com/paper/2306.08630/) (2023)
- [Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems](https://mlsanity.com/paper/2202.05311/) (2022)
- [Advancing the AmbientGAN for learning stochastic object models](https://mlsanity.com/paper/2102.00281/) (2021)
- [Prior image-based medical image reconstruction using a style-based generative adversarial network](https://mlsanity.com/paper/2202.08936/) (2022)
- [Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging](https://mlsanity.com/paper/2207.10181/) (2022)
- [Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz](https://mlsanity.com/paper/2305.07618/) (2023)
- [Learning Generative Prior with Latent Space Sparsity Constraints](https://mlsanity.com/paper/2105.11956/) (2021)
- [Regularized Training of Intermediate Layers for Generative Models for Inverse Problems](https://mlsanity.com/paper/2203.04382/) (2022)
- [Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging](https://mlsanity.com/paper/2204.03547/) (2022)
- [Latent Space Imaging](https://mlsanity.com/paper/2407.07052/) (2024)
- [ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging](https://mlsanity.com/paper/2505.22683/) (2025)
- [MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution](https://mlsanity.com/paper/2212.00069/) (2022)

## Abstract (arXiv)

There remains an important need for the development of image reconstruction methods that can produce diagnostically useful images from undersampled measurements. In magnetic resonance imaging (MRI), for example, such methods can facilitate reductions in data-acquisition times. Deep learning-based methods hold potential for learning object priors or constraints that can serve to mitigate the effects of data-incompleteness on image reconstruction. One line of emerging research involves formulating an optimization-based reconstruction method in the latent space of a generative deep neural network. However, when generative adversarial networks (GANs) are employed, such methods can result in image reconstruction errors if the sought-after solution does not reside within the range of the GAN. To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models. A novel regularization strategy is introduced that takes advantage of the multiscale architecture of certain invertible neural networks, which can result in improved reconstruction performance over classical methods in terms of traditional metrics. The proposed method is investigated for reconstructing images from undersampled MRI data. The method is shown to achieve comparable performance to a state-of-the-art generative model-based reconstruction method while benefiting from a deterministic reconstruction procedure and easier control over regularization parameters.