---
license: mit
language:
  - en
pipeline_tag: image-to-image
library_name: pytorch

tags:
  - medical-imaging
  - computer-vision
  - pytorch
  - pix2pix
  - image-enhancement
  - laparoscopy
  - surgical-smoke-removal
  - defogging
  - gan
  - deep-learning
  - opencv
  - healthcare-ai

datasets:
  - custom

metrics:
  - psnr
  - ssim
---

# Laparoscopy Image Defogging AI

An AI-powered laparoscopic image enhancement system designed to remove fog, haze, and surgical smoke from minimally invasive surgical imagery using deep learning and image restoration techniques.

This repository contains pretrained Pix2Pix UNet-256 generator weights for real-time laparoscopic image defogging and enhancement.

---

# Model Details

## Model Description

This model is designed to improve the visual clarity of laparoscopic surgical images by removing:
- Lens fogging
- Surgical smoke
- Haze
- Low contrast artifacts

The system combines:
- Pix2Pix GAN image translation
- Dark Channel Prior (DCP)
- Guided filtering
- CLAHE enhancement
- Contrast restoration
- Sharpening and post-processing

The model aims to enhance visibility in minimally invasive surgical environments for research and educational applications.

---

- **Developed by:** Vishnu Das
- **Model type:** Pix2Pix GAN / UNet-256 Generator
- **Framework:** PyTorch
- **Language(s):** English
- **License:** MIT
- **Task:** Image-to-Image Translation / Medical Image Enhancement

---

# Model Sources

- **Repository:** https://github.com/YOUR_GITHUB_USERNAME/YOUR_REPOSITORY_NAME
- **Model Repository:** https://huggingface.co/vishnudaspk/Laparoscopy-Image-Defogging-AI

---

# Uses

## Direct Use

This model can be used for:
- Laparoscopic image enhancement
- Surgical smoke removal
- Fog removal
- Medical imaging research
- Computer vision experimentation
- Deep learning demonstrations

---

## Downstream Use

Possible downstream applications:
- Real-time surgical visualization systems
- AI-assisted medical imaging pipelines
- Surgical simulation environments
- Medical video enhancement workflows

---

## Out-of-Scope Use

This model is NOT intended for:
- Clinical diagnosis
- Real surgical deployment
- Medical decision-making
- Autonomous healthcare systems

Outputs should always be reviewed by qualified professionals.

---

# Bias, Risks, and Limitations

- Performance depends heavily on image quality and training distribution.
- The model may produce artifacts under severe smoke or lighting conditions.
- Results may vary across different laparoscopic devices and environments.
- This system is intended for research and educational purposes only.

---

# Recommendations

Users should:
- Validate outputs before use
- Avoid clinical reliance
- Test across multiple datasets
- Use CUDA-enabled GPUs for best performance

---

# How to Get Started with the Model

## Installation

```bash
pip install -r requirements.txt
```

Recommended:

* NVIDIA GPU
* CUDA-enabled PyTorch

---

## Place Model Weights

Place:

```text
best_net_G.pth
```

inside:

```text
scripts/checkpoints/pix2pix_laparoscopy_dc/
```

---

## Run Application

```bash
python app.py
```

Open:

```text
http://127.0.0.1:5000
```

---

# Training Details

## Training Data

The model was trained on custom laparoscopic imagery containing varying levels of:

* Surgical smoke
* Fogging
* Low visibility
* Illumination artifacts

Data preprocessing included:

* Resizing
* Contrast normalization
* Paired image generation

---

## Training Procedure

### Preprocessing

* Image normalization
* CLAHE enhancement
* Resizing
* Data augmentation

---

### Training Hyperparameters

* **Architecture:** Pix2Pix UNet-256
* **Framework:** PyTorch
* **Training regime:** Mixed precision CUDA training
* **Loss Functions:** GAN Loss + L1 Loss

---

# Evaluation

## Testing Data, Factors & Metrics

### Testing Data

Custom laparoscopic test imagery.

---

### Metrics

Evaluation metrics include:

* PSNR
* SSIM
* Visual perceptual quality

---

# Results

The model demonstrated:

* Improved image clarity
* Reduced haze and smoke artifacts
* Enhanced contrast and edge visibility

---

# Environmental Impact

Training performed on:

* **Hardware Type:** NVIDIA RTX 4060 GPU
* **Framework:** PyTorch CUDA

---

# Technical Specifications

## Model Architecture and Objective

* Pix2Pix GAN
* UNet-256 Generator
* Image-to-image translation objective

---

## Compute Infrastructure

### Hardware

* NVIDIA RTX 4060 Laptop GPU

### Software

* Python
* PyTorch
* OpenCV
* NumPy
* Flask

---

# Citation

If you use this project in research or educational work, please cite the repository appropriately.

---

# More Information

This project was developed as a deep learning and medical imaging research initiative focused on improving surgical visualization quality using AI-powered enhancement techniques.

---

# Model Card Authors

Vishnu Das

---

# Model Card Contact

For questions or collaboration:

* GitHub: [https://github.com/YOUR_GITHUB_USERNAME](https://github.com/YOUR_GITHUB_USERNAME)