# HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning

arXiv: https://arxiv.org/abs/2411.18296 · HTML: https://mlsanity.com/paper/2411.18296/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 78 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 30 of 32)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A Fourier-based Framework for Domain Generalization](https://mlsanity.com/paper/2105.11120/) (2021)
- [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)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://mlsanity.com/paper/2007.08103/) (2020)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Single-Shot Refinement Neural Network for Object Detection](https://mlsanity.com/paper/1711.06897/) (2017)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://mlsanity.com/paper/1811.08201/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [Unsupervised Lesion Detection via Image Restoration with a Normative Prior](https://mlsanity.com/paper/2005.00031/) (2020)
- [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022)
- [LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1905.02423/) (2019)
- [A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion](https://mlsanity.com/paper/2305.15862/) (2023)
- [Value-Function-based Sequential Minimization for Bi-level Optimization](https://mlsanity.com/paper/2110.04974/) (2021)
- [Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing](https://mlsanity.com/paper/2308.06998/) (2023)
- [Underwater Image Enhancement based on Deep Learning and Image Formation Model](https://mlsanity.com/paper/2101.00991/) (2021)
- [Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles](https://mlsanity.com/paper/2302.07344/) (2023)
- [Underwater Image Restoration via Contrastive Learning and a Real-world Dataset](https://mlsanity.com/paper/2106.10718/) (2021)
- [Automated Visual Fin Identification of Individual Great White Sharks](https://mlsanity.com/paper/1609.06323/) (2016)
- [Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement](https://mlsanity.com/paper/2308.11932/) (2023)
- [Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion](https://mlsanity.com/paper/2309.01113/) (2023)
- [Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement](https://mlsanity.com/paper/2404.17400/) (2024)
- [Self-Supervised Fast Adaptation for Denoising via Meta-Learning](https://mlsanity.com/paper/2001.02899/) (2020)
- [Context-Driven Detection of Invertebrate Species in Deep-Sea Video](https://mlsanity.com/paper/2206.00718/) (2022)
- [WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond](https://mlsanity.com/paper/2308.00931/) (2023)

## Built on by (most cited first, 7 of 7)

- [Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design](https://mlsanity.com/paper/2603.01767/) (2026)
- [Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach](https://mlsanity.com/paper/2503.17937/) (2025)
- [Depth-Supervised Fusion Network for Seamless-Free Image Stitching](https://mlsanity.com/paper/2510.21396/) (2025)
- [UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection](https://mlsanity.com/paper/2605.07146/) (2026)
- [A Dual-Branch Collaborative Framework for Joint Optimization of Underwater Image Enhancement and Object Detection](https://mlsanity.com/paper/2606.15857/) (2026)
- [DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement](https://mlsanity.com/paper/2607.22801/) (2026)
- [PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration](https://mlsanity.com/paper/2610.17991/) (2026)

## Abstract (arXiv)

Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications. To strike a balance between visual quality and application, we propose a heuristic invertible network for underwater perception enhancement, dubbed HUPE, which enhances visual quality and demonstrates flexibility in handling other downstream tasks. Specifically, we introduced an information-preserving reversible transformation with embedded Fourier transform to establish a bidirectional mapping between underwater images and their clear images. Additionally, a heuristic prior is incorporated into the enhancement process to better capture scene information. To further bridge the feature gap between vision-based enhancement images and application-oriented images, a semantic collaborative learning module is applied in the joint optimization process of the visual enhancement task and the downstream task, which guides the proposed enhancement model to extract more task-oriented semantic features while obtaining visually pleasing images. Extensive experiments, both quantitative and qualitative, demonstrate the superiority of our HUPE over state-of-the-art methods. The source code is available at https://github.com/ZengxiZhang/HUPE.