# Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction

arXiv: https://arxiv.org/abs/2402.04097 · HTML: https://mlsanity.com/paper/2402.04097/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 30 of 30)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks](https://mlsanity.com/paper/1810.03982/) (2018)
- [Image inpainting: A review](https://mlsanity.com/paper/1909.06399/) (2019)
- [DeepRED: Deep Image Prior Powered by RED](https://mlsanity.com/paper/1903.10176/) (2019)
- [Image Restoration using Total Variation Regularized Deep Image Prior](https://mlsanity.com/paper/1810.12864/) (2018)
- [Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning](https://mlsanity.com/paper/1904.02816/) (2019)
- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [A Bayesian Perspective on the Deep Image Prior](https://mlsanity.com/paper/1904.07457/) (2019)
- [Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators](https://mlsanity.com/paper/1910.14634/) (2019)
- [Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation](https://mlsanity.com/paper/2005.03991/) (2020)
- [Rethinking Deep Image Prior for Denoising](https://mlsanity.com/paper/2108.12841/) (2021)
- [On Measuring and Controlling the Spectral Bias of the Deep Image Prior](https://mlsanity.com/paper/2107.01125/) (2021)
- [Equivariant plug-and-play image reconstruction](https://mlsanity.com/paper/2312.01831/) (2023)
- [Wasserstein GANs for MR Imaging: from Paired to Unpaired Training](https://mlsanity.com/paper/1910.07048/) (2019)
- [Scaling Laws For Deep Learning Based Image Reconstruction](https://mlsanity.com/paper/2209.13435/) (2022)
- [The Neural Tangent Link Between CNN Denoisers and Non-Local Filters](https://mlsanity.com/paper/2006.02379/) (2020)
- [Better NTK Conditioning: A Free Lunch from (ReLU) Nonlinear Activation in Wide Neural Networks](https://mlsanity.com/paper/2305.08813/) (2023)
- [Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction](https://mlsanity.com/paper/2104.05028/) (2021)
- [Bilevel learning of l1-regularizers with closed-form gradients(BLORC)](https://mlsanity.com/paper/2111.10858/) (2021)
- [Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data](https://mlsanity.com/paper/2206.00775/) (2022)

## Built on by (most cited first, 11 of 11)

- [SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems](https://mlsanity.com/paper/2410.04479/) (2024)
- [UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weights](https://mlsanity.com/paper/2505.11720/) (2025)
- [What Your Features Reveal: Data-Efficient Black-Box Feature Inversion Attack for Split DNNs](https://mlsanity.com/paper/2511.15316/) (2025)
- [MoRe-3DGSMR: Motion-resolved reconstruction framework for free-breathing pulmonary MRI based on 3D Gaussian representation](https://mlsanity.com/paper/2505.04959/) (2025)
- [Deep Spectral Prior](https://mlsanity.com/paper/2505.19873/) (2025)
- [On the Dataless Training of Neural Networks](https://mlsanity.com/paper/2510.25962/) (2025)
- [Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstruction](https://mlsanity.com/paper/2512.03962/) (2025)
- [Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising](https://mlsanity.com/paper/2604.08272/) (2026)
- [A Principled Self-Referenced Early Stopping Approach for Deep Image Prior](https://mlsanity.com/paper/2605.25299/) (2026)
- [Trajectory Constraints for Imaging Inverse Problems](https://mlsanity.com/paper/2605.29012/) (2026)
- [Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior](https://mlsanity.com/paper/2609.01959/) (2026)

## Abstract (arXiv)

The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging including magnetic resonance imaging (MRI). However, conventional DIP suffers from severe overfitting and spectral bias effects. In this work, we first provide an analysis of how DIP recovers information from undersampled imaging measurements by analyzing the training dynamics of the underlying networks in the kernel regime for different architectures. This study sheds light on important underlying properties for DIP-based recovery. Current research suggests that incorporating a reference image as network input can enhance DIP's performance in image reconstruction compared to using random inputs. However, obtaining suitable reference images requires supervision, and raises practical difficulties. In an attempt to overcome this obstacle, we further introduce a self-driven reconstruction process that concurrently optimizes both the network weights and the input while eliminating the need for training data. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image. We demonstrate that our self-guided method surpasses both the original DIP and modern supervised methods in terms of MR image reconstruction performance and outperforms previous DIP-based schemes for image inpainting.