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README

HUPE

This is an implementation of the paper, HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning", Zengxi Zhang, Zhiying Jiang, Long Ma, Jinyuan Liu, Xin Fan, Risheng Liu*, 2025, International Journal of Computer Vision.

Overview

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Download

Download the pre-trained model and put it in experiments/ - Baidu Yun \ code: ​1ecu ​ - Google Drive

Quick Run

To test the pre-trained models for Underwater Enhancement on example images:

Step 1: Generated priors from with underwater images:

run file 'IR_GDCP_forfolder.m' from root 'code/Generalization-of-the-Dark-Channel-Prior-for-Single-Image-Restoration-main/'

Step 2: Generated enhancement images:

cd code
python test.py

Contact

If you find our work useful in your research, please cite our paper:

@article{zhang2025hupe,
  title={HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning},
  author={Zhang, Zengxi and Jiang, Zhiying and Ma, Long and Liu, Jinyuan and Fan, Xin and Liu, Risheng},
  journal={International Journal of Computer Vision},
  pages={1--19},
  year={2025},
  publisher={Springer}
}

Contact

Should you have any question, please contact [Zengxi Zhang].

Core symbols most depended-on inside this repo

opt_get
called by 34
code/utils/util.py
to
called by 8
code/models/LLFlow_model.py
unsqueeze2d
called by 7
code/models/modules/flow.py
get_module
called by 5
code/models/LLFlow_model.py
get_logdet
called by 4
code/models/modules/FlowAffineCouplingsAblation.py
create_window
called by 3
code/utils/util.py
create_window
called by 3
code/models/modules/loss.py
getConditional
called by 3
code/models/modules/FlowStep.py

Shape

Method 182
Class 65
Function 57

Languages

Python100%

Modules by API surface

code/models/modules/base_layers.py35 symbols
code/models/modules/loss.py33 symbols
code/utils/util.py27 symbols
code/models/modules/FlowAffineCouplingsAblation.py19 symbols
code/models/modules/FlowUpsamplerNet.py18 symbols
code/models/modules/flow.py17 symbols
code/models/modules/cbam_fromdehazeflow.py17 symbols
code/models/modules/ConditionEncoder.py16 symbols
code/utils/timer.py15 symbols
code/models/modules/FlowActNorms.py13 symbols
code/models/modules/color_encoder.py9 symbols
code/models/modules/RRDBNet_arch.py9 symbols

For agents

$ claude mcp add HUPE \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact

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