# Shallow-UWnet : Compressed Model for Underwater Image Enhancement

arXiv: https://arxiv.org/abs/2101.02073 · HTML: https://mlsanity.com/paper/2101.02073/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception](https://mlsanity.com/paper/2002.01155/) (2020)

## Built on by (most cited first, 30 of 35)

- [Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model](https://mlsanity.com/paper/2311.11638/) (2023)
- [Underwater Light Field Retention : Neural Rendering for Underwater Imaging](https://mlsanity.com/paper/2203.11006/) (2022)
- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [UniUIR: Considering Underwater Image Restoration as An All-in-One Learner](https://mlsanity.com/paper/2501.12981/) (2025)
- [UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration](https://mlsanity.com/paper/2505.06683/) (2025)
- [UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement](https://mlsanity.com/paper/2404.14542/) (2024)
- [Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis](https://mlsanity.com/paper/2501.02701/) (2025)
- [MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices](https://mlsanity.com/paper/2507.01838/) (2025)
- [UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer](https://mlsanity.com/paper/2310.20210/) (2023)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [O-Mamba: O-shape State-Space Model for Underwater Image Enhancement](https://mlsanity.com/paper/2408.12816/) (2024)
- [MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration](https://mlsanity.com/paper/2401.10666/) (2024)
- [RGB-Sonar Tracking Benchmark and Spatial Cross-Attention Transformer Tracker](https://mlsanity.com/paper/2406.07189/) (2024)
- [PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement](https://mlsanity.com/paper/2406.08444/) (2024)
- [MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs](https://mlsanity.com/paper/2404.13884/) (2024)
- [RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement](https://mlsanity.com/paper/2302.12186/) (2023)
- [Underwater Image Enhancement by Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2503.20485/) (2025)
- [A Survey of Fish Tracking Techniques Based on Computer Vision](https://mlsanity.com/paper/2110.02551/) (2021)
- [A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior](https://mlsanity.com/paper/2405.16197/) (2024)
- [Image-Conditional Diffusion Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2407.05389/) (2024)
- [Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement](https://mlsanity.com/paper/2507.06234/) (2025)
- [USLN: A statistically guided lightweight network for underwater image enhancement via dual-statistic white balance and multi-color space stretch](https://mlsanity.com/paper/2209.02221/) (2022)
- [Underwater Image Enhancement with Physical-based Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2409.18476/) (2024)
- [Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.19895/) (2025)
- [On the Global Photometric Alignment for Low-Level Vision](https://mlsanity.com/paper/2604.08172/) (2026)
- [HIFI-Net: A Novel Network for Enhancement to Underwater Images](https://mlsanity.com/paper/2206.02295/) (2022)
- [IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater](https://mlsanity.com/paper/2312.06955/) (2023)
- [snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing](https://mlsanity.com/paper/2504.11482/) (2025)
- [Image Restoration via Multi-domain Learning](https://mlsanity.com/paper/2505.05504/) (2025)
- [RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation](https://mlsanity.com/paper/2509.23480/) (2025)

## Abstract (arXiv)

Over the past few decades, underwater image enhancement has attracted increasing amount of research effort due to its significance in underwater robotics and ocean engineering. Research has evolved from implementing physics-based solutions to using very deep CNNs and GANs. However, these state-of-art algorithms are computationally expensive and memory intensive. This hinders their deployment on portable devices for underwater exploration tasks. These models are trained on either synthetic or limited real world datasets making them less practical in real-world scenarios. In this paper we propose a shallow neural network architecture, Shallow-UWnet which maintains performance and has fewer parameters than the state-of-art models. We also demonstrated the generalization of our model by benchmarking its performance on combination of synthetic and real-world datasets.