# AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration

arXiv: https://arxiv.org/abs/2402.03738 · HTML: https://mlsanity.com/paper/2402.03738/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 79 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Low-Light Image Enhancement via Structure Modeling and Guidance](https://mlsanity.com/paper/2305.05839/) (2023)
- [Underwater Ranker: Learn Which Is Better and How to Be Better](https://mlsanity.com/paper/2208.06857/) (2022)
- [Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey](https://mlsanity.com/paper/1611.05842/) (2016)
- [Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement](https://mlsanity.com/paper/2112.05147/) (2021)
- [SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing](https://mlsanity.com/paper/2304.08444/) (2023)
- [MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition](https://mlsanity.com/paper/2304.14857/) (2023)
- [Single Image Dehazing with An Independent Detail-Recovery Network](https://mlsanity.com/paper/2109.10492/) (2021)
- [Variational Regularized Transmission Refinement for Image Dehazing](https://mlsanity.com/paper/1902.07069/) (2019)
- [LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement](https://mlsanity.com/paper/1906.05119/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [Multi-Knowledge-oriented Nighttime Haze Imaging Enhancer for Vision-driven Intelligent Systems](https://mlsanity.com/paper/2502.07351/) (2025)
- [Real-World Scene Recovery for Scattering-Degraded Images Using Spatial and Frequency Priors](https://mlsanity.com/paper/2512.08254/) (2025)

## Abstract (arXiv)

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet) to improve the visibility of imaging devices in typical low-visibility imaging scenes (e.g., haze, sand dust, and low light). It combines gamma correction (GC) and optimized linear stretching (OLS) to create the detail enhancement module (DEM) and color restoration module (CRM). Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations. Finally, we refine the coarse features generated by DEM, CRM, and MEM through Encoder-Decoder to generate the final restored image. Comprehensive experimental results demonstrate the effectiveness and stability of AoSRNet compared to other state-of-the-art methods. The source code is available at urlhttps://github.com/LouisYuxuLu/AoSRNet.