# Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM

arXiv: https://arxiv.org/abs/2009.11380 · HTML: https://mlsanity.com/paper/2009.11380/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent](https://mlsanity.com/paper/2204.13940/) (2022)
- [Unsupervised Knowledge-Transfer for Learned Image Reconstruction](https://mlsanity.com/paper/2107.02572/) (2021)
- [Wiener Guided DIP for Unsupervised Blind Image Deconvolution](https://mlsanity.com/paper/2112.10271/) (2021)
- [Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc](https://mlsanity.com/paper/2306.14209/) (2023)
- [Boosting of Implicit Neural Representation-based Image Denoiser](https://mlsanity.com/paper/2401.01548/) (2024)
- [A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on n-dimensional Classes](https://mlsanity.com/paper/2404.06104/) (2024)
- [Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization](https://mlsanity.com/paper/2406.05288/) (2024)
- [MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction](https://mlsanity.com/paper/2304.03895/) (2023)
- [RELD: Regularization by Latent Diffusion Models for Image Restoration](https://mlsanity.com/paper/2503.22563/) (2025)
- [Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models](https://mlsanity.com/paper/2506.13391/) (2025)
- [3DeepRep: 3D Deep Low-rank Tensor Representation for Hyperspectral Image Inpainting](https://mlsanity.com/paper/2506.16735/) (2025)
- [Deep Image Prior with L0 Gradient Regularizer for Image Smoothing](https://mlsanity.com/paper/2601.13400/) (2026)

## Abstract (arXiv)

In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount of training examples is not always feasible. Moreover, the construction of a good training set is time consuming and hard because the selected data have to be enough representative for the task. In this paper, we focus on the Deep Image Prior (DIP) framework and we propose to combine it with a space-variant Total Variation regularizer with an automatic estimation of the local regularization parameters. Differently from other existing approaches, we solve the arising minimization problem via the flexible Alternating Direction Method of Multipliers (ADMM). Furthermore, we provide a specific implementation also for the standard isotropic Total Variation. The promising performances of the proposed approach, in terms of PSNR and SSIM values, are addressed through several experiments on simulated as well as real natural and medical corrupted images.