🔧 All-Image-Enhance-API

A plug-and-play API system for state-of-the-art Image Enhancement and Restoration models, designed to run on both GPU and CPU with optimized latency (<10ms target).

🎯 Project Goal

Build a modular API interface for image enhancement tasks (e.g., image enhancement, super-resolution, denoising, deblurring), wrapping existing models like:

• BSRGAN
• Real-ESRGAN
• ESRGAN+
• SwinIR
• DnCNN

Each model should be accessible via a unified API endpoint.

---

📦 Folder Structure (Recommended)

ALL-Enhance-API/
├── models/
│   ├── real\_esrgan/
│   └── bsrgan/
├── enhancer/
│   └── base.py         # Base class for loading and using models
├── api/
│   └── main.py         # FastAPI/Flask entrypoint
├── utils/
│   └── benchmark.py    # Measure latency
├── requirements.txt
├── README.md
└── run.sh              # Script to run the API

---

🚀 Quick Start

1. Clone the repo

git clone https://github.com/your-org/ALL-Enhance-API.git
cd ALL-Enhance-API

2. Setup Environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

3. Run API Server

cd api
uvicorn main:app --reload  # For FastAPI
# or
python main.py             # For Flask

---

🖼️ API Usage

POST /enhance

Request:

{
  "image": "<base64-encoded image>",
  "model": "real-esrgan",
  "device": "cuda"   // or "cpu"
}

Response:

{
  "output_image": "<base64-encoded enhanced image>",
  "latency_ms": 8.4
}

---

⚙️ Current Supported Models

Model	Task	Notes
Real-ESRGAN	Super-resolution	Stable, fast
BSRGAN	Blind SR	Handles real-world noise
SwinIR	SR + Denoising	Large, needs tuning

---

📊 Benchmarking

Use utils/benchmark.py to evaluate latency on different devices and image sizes.

python utils/benchmark.py --model real-esrgan --device cuda --size 512

---

📌 To-Do

• Add ONNX/TensorRT optimization support
• Add batch processing support
• Add async queueing with Redis (optional)
• Dockerize the API for deployment

---

🧠 Notes for Contributors

• Follow PEP8
• Keep model wrappers clean and modular
• Avoid hardcoded paths; use config.yaml or CLI args

Estimated inference Runtime on CPU

https://docs.google.com/spreadsheets/d/1RmzOShrssWUb2wrY3Pk115VARq6SE8ObDFn3pWDKTXA/edit?usp=sharinghttps://docs.github.com/github/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax