# Hypernetwork-Based Adaptive Image Restoration

arXiv: https://arxiv.org/abs/2206.05970 · HTML: https://mlsanity.com/paper/2206.05970/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Meta-SR: A Magnification-Arbitrary Network for Super-Resolution](https://mlsanity.com/paper/1903.00875/) (2019)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [Deep Network Interpolation for Continuous Imagery Effect Transition](https://mlsanity.com/paper/1811.10515/) (2018)
- [Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers](https://mlsanity.com/paper/1904.08118/) (2019)
- [CFSNet: Toward a Controllable Feature Space for Image Restoration](https://mlsanity.com/paper/1904.00634/) (2019)
- [MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation](https://mlsanity.com/paper/2008.12094/) (2020)
- [Decouple Learning for Parameterized Image Operators](https://mlsanity.com/paper/1807.08186/) (2018)
- [Dynamic-Net: Tuning the Objective Without Re-training for Synthesis Tasks](https://mlsanity.com/paper/1811.08760/) (2018)
- [Smoother Network Tuning and Interpolation for Continuous-level Image Processing](https://mlsanity.com/paper/2010.02270/) (2020)

## Built on by (most cited first, 8 of 8)

- [HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling](https://mlsanity.com/paper/2403.13319/) (2024)
- [HAIR: Hypernetworks-based All-in-One Image Restoration](https://mlsanity.com/paper/2408.08091/) (2024)
- [Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA](https://mlsanity.com/paper/2411.00259/) (2024)
- [GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary Views](https://mlsanity.com/paper/2407.08221/) (2024)
- [HyDA: Hypernetworks for Test Time Domain Adaptation in Medical Imaging Analysis](https://mlsanity.com/paper/2503.04979/) (2025)
- [ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep Networks](https://mlsanity.com/paper/2411.10891/) (2024)
- [SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution](https://mlsanity.com/paper/2411.15659/) (2024)
- [Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroups](https://mlsanity.com/paper/2601.13094/) (2026)

## Abstract (arXiv)

Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution.