# Neural Architecture Search for Deep Image Prior

arXiv: https://arxiv.org/abs/2001.04776 · HTML: https://mlsanity.com/paper/2001.04776/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [On Measuring and Controlling the Spectral Bias of the Deep Image Prior](https://mlsanity.com/paper/2107.01125/) (2021)
- [Neural Architecture Search for Dense Prediction Tasks in Computer Vision](https://mlsanity.com/paper/2202.07242/) (2022)
- [The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior](https://mlsanity.com/paper/2304.11409/) (2023)
- [Blind Image Deconvolution Using Variational Deep Image Prior](https://mlsanity.com/paper/2202.00179/) (2022)
- [Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization](https://mlsanity.com/paper/2312.09988/) (2023)
- [HOIN: High-Order Implicit Neural Representations](https://mlsanity.com/paper/2404.14674/) (2024)
- [Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search](https://mlsanity.com/paper/2105.07768/) (2021)
- [Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview](https://mlsanity.com/paper/2402.12072/) (2024)
- [Deep Learning with Partially Labeled Data for Radio Map Reconstruction](https://mlsanity.com/paper/2306.05294/) (2023)
- [DIPLI: Deep Image Prior Lucky Imaging for Blind Astronomical Image Restoration](https://mlsanity.com/paper/2503.15984/) (2025)
- [Automating Parameter Selection in Deep Image Prior for Fluorescence Microscopy Image Denoising via Similarity-Based Parameter Transfer](https://mlsanity.com/paper/2601.12055/) (2026)
- [Pooling-Based Context Modeling for Convolution-Free Deep Image Prior](https://mlsanity.com/paper/2607.02952/) (2026)

## Abstract (arXiv)

We present a neural architecture search (NAS) technique to enhance the performance of unsupervised image de-noising, in-painting and super-resolution under the recently proposed Deep Image Prior (DIP). We show that evolutionary search can automatically optimize the encoder-decoder (E-D) structure and meta-parameters of the DIP network, which serves as a content-specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E-D network that typically converges to yield a content-specific DIP within 10-20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.