# Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning

arXiv: https://arxiv.org/abs/2305.18708 · HTML: https://mlsanity.com/paper/2305.18708/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Spatio-Temporal Filter Adaptive Network for Video Deblurring](https://mlsanity.com/paper/1904.12257/) (2019)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Spatially-Adaptive Image Restoration using Distortion-Guided Networks](https://mlsanity.com/paper/2108.08617/) (2021)
- [Online Video Deblurring via Dynamic Temporal Blending Network](https://mlsanity.com/paper/1704.03285/) (2017)
- [Dynamic Attentive Graph Learning for Image Restoration](https://mlsanity.com/paper/2109.06620/) (2021)
- [Patch Craft: Video Denoising by Deep Modeling and Patch Matching](https://mlsanity.com/paper/2103.13767/) (2021)

## Abstract (arXiv)

Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without external knowledge. Its wide & deep architecture uses these learned parameters to directly modulate restoration, achieving spatially and intensity adaptive results. Evaluated on dedicated infrared deturbulence (49,744 images) and visible image denoising (109,536 images) datasets, DparNet significantly outperforms State-of-the-Art (SOTA) methods in restoration performance and efficiency. Notably, leveraging these parameters improves PSNR by 0.6-1.1 dB with less than 2% increase in model parameters and computational complexity. Our work demonstrates that degraded images hide key degradation information that can be learned and utilized to boost adaptive image restoration.