# Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

@inproceedings{Jagatap2019AlgorithmicGF, title={Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors}, author={Gauri Jagatap and Chinmay Hegde}, booktitle={NeurIPS}, year={2019} }

Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafted image priors such as sparsity and low-rank. [...] Key Method We model images to lie in the range of an untrained deep generative network with a fixed seed. Expand

## 30 Citations

Phase Retrieval using Untrained Neural Network Priors

- Computer Science
- 2019

This paper considers the non-linear inverse problem of compressive phase retrieval (CPR), and model images to lie in the range of an untrained deep generative network with a fixed seed, and presents two approaches for solving CPR — gradient descent, and projected gradient descent.

Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

- Computer Science, MathematicsArXiv
- 2019

This work considers solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest and shows how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization.

On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks

- Computer Science, MedicineEntropy
- 2021

This paper seeks to broaden the applicability and understanding of untrained neural network priors by investigating the interaction between architecture selection, measurement models, and signal types by investigating which hyperparameters tend to be more important, and which are robust to deviations from the optimum.

High Dynamic Range Imaging Using Deep Image Priors

- Computer ScienceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2020

Two different approaches to high dynamic range (HDR) imaging are considered – gamma encoding and modulo encoding – and a combination of deep image prior and total variation (TV) regularization for reconstructing low-light images is proposed.

Regularizing linear inverse problems with convolutional neural networks

- Computer Science, MathematicsArXiv
- 2019

This paper demonstrates that signal recovery with a un-trained convolutional network outperforms standard l1 and total variation minimization for magnetic resonance imaging (MRI) and shows that, similar to standard compressive sensing guarantees, on the order of the number of model parameters many measurements suffice for recovering an image from compressive measurements.

DAEs for Linear Inverse Problems: Improved Recovery with Provable Guarantees

- Computer Science, EngineeringArXiv
- 2021

This work uses Denoising Auto Encoders as priors and a projected gradient descent algorithm for recovering the original signal and finds that the algorithm speeds up recovery by two orders of magnitude, improves quality of reconstruction by an order of magnitude), and does not require tuning hyperparameters.

Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation

- Computer Science, EngineeringICML
- 2020

It is shown that---without any further regularization---an un-trained convolutional neural network can approximately reconstruct signals and images that are sufficiently structured, from a near minimal number of random measurements.

Unrolled Wirtinger Flow with Deep Priors for Phaseless Imaging

- Engineering
- 2021

We introduce a deep learning (DL) based network for imaging from measurement intensities. The network architecture uses a recurrent structure that unrolls the Wirtinger Flow (WF) algorithm with a…

Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators

- Computer Science, MathematicsICLR
- 2020

A step towards demystifying this experimental phenomenon is taken by attributing this effect to particular architectural choices of convolutional networks, namely convolutions with fixed interpolating filters, and it is proved that early-stopped gradient descent denoises/regularizes.

Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models

- Computer Science, MathematicsArXiv
- 2021

This work establishes a simple nonconvex algorithmic approach that theoretically enjoys linear convergence guarantees for certain linear and nonlinear inverse problems, and empirically improves upon conventional techniques such as back-propagation.

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