UDBE

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A diffusion model with the capability to enhance bright features in images

UDBE 최신버젼 다운로드

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# **UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images**

Tatiana Tais Schein, tatischein@furg.br | Gustavo Pereira de Almeira, gustavo.pereira.furg@furg.br | Stephanie Loi Briao, stephanie.loi@furg.br | Felipe Gomes de Oliveira, felipeoliveira@ufam.edu.br | Rodrigo Andrade de Bem, rodrigo.bem@gmail.com | Paulo L. J. Drews-Jr, paulodrews@furg.br

## Overview
Activities in underwater environments are paramount in several scenarios, which drives the continuous development of underwater image enhancement techniques. A major challenge in this domain is the depth at which images are captured, with increasing depth resulting in a darker environment. Most existing methods for underwater image enhancement focus on noise removal and color adjustment, with few works dedicated to brightness  enhancement. This work introduces a novel unsupervised learning approach to underwater image enhancement using a diffusion model. Our method, called UDBE, is based on conditional diffusion to maintain the brightness  etails of the unpaired input images. The input image is combined with a color map and a SignalNoise Relation map (SNR) to ensure stable training and prevent  color distortion in the output images. The results demonstrate that our approach achieves an impressive accuracy rate in the datasets UIEB, SUIM and RUIE, well-established underwater image benchmarks. Additionally, the experiments validate the robustness of our approach, regarding  he image quality metrics PSNR, SSIM, UIQM, and UISM, indicating the good performance of the brightness enhancement process.

## Data Structure

## Setup

'''
git clone https://github.com/gusanagy/UDBE.git   
'''

'''
cd UDBE
'''

'''
conda env create -f UDBE.yaml --name UDBE
conda activate UDBE
'''

## Usage

Our code was trained on a computer with two NVIDIA TITAN X GPUs, totaling 24GB of GPU memory.

```
python #train from scratch, you can change setting in modelConfig 
python main.py --dataset_path "" --dataset "UIEB" -- state "train" 
python main.py --dataset_path "" --dataset "SUIM" -- state "train"

python main.py --pretrained_path  --dataset "SUIM" -- state "val" --pretrained_path "1000.pt"
python main.py --pretrained_path  --dataset "UIEB" -- state "val" --pretrained_path "1000.pt"

python main.py --pretrained_path  --dataset "SUIM" -- state "inference" --inference_image " "  --pretrained_path "1000.pt"
python main.py --pretrained_path  --dataset "UIEB" -- state "inference" --inference_image " " --pretrained_path "1000.pt"

```

Tests can be done in the notebook [avaliacao.ipynb](avaliacao.ipynb). As well as viewing images from the respective datasets. When running this notebook there are options to download and automatically generate the folders, datasets and checkpoints necessary to run the model.

<!--
# Mask CLE Diffusion
Mask CLE Diffusion finetunes lol checkpoint. In our experiments, lol checkpoint is better than mit-adobe-5K checkpoint.

We show some inference cases in 'data/Mask_CLE_cases'. Welcome to use your cases to test the performance.
 /*We show some inference cases in 'data/Mask_CLE_cases'. Welcome to use your cases to test the performance.

```python
python mask_generation.py   #generate masks for training
python train_mask.py --pretrained_path ckpt/lol.pt  #finetune Mask CLE Diffusion
python test_mask.py --pretrained_path ckpt/Mask_CLE.pt --input_path data/Mask_CLE_cases/opera.png --mask_path data/Mask_CLE_cases/opera_mask.png --data_name opera
```
*/ -->

Paper: https://arxiv.org/abs/2501.16211
For quotes:

'''
@article{schein2025udbe,
  title={UDBE: Unsupervised Diffusion-based Brightness Enhancement in Underwater Images},
  author={Schein, Tatiana Ta{\'\i}s and de Almeira, Gustavo Pereira and Bri{\~a}o, Stephanie Loi and de Bem, Rodrigo Andrade and de Oliveira, Felipe Gomes and Drews-Jr, Paulo LJ},
  journal={arXiv preprint arXiv:2501.16211},
  year={2025}
}
'''

# ACKNOWLEDGMENT
This study was funded, in part, by the São Paulo Research Foundation (FAPESP), Brazil, under Process Number 2024/10523-5. The authors would also like to thank the PRH-ANP and CNPQ organizations for their research support and financial assistance.

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