Building an AI Image Enhancement Platform
7 min readNov 5, 2024

Building an AI Image Enhancement Platform

HZ
Huzaifa Zafar · Senior Frontend Engineer

The Challenge


Refinae needed to handle multiple enhancement pipelines including face restoration, noise reduction, sharpening, color correction and upscaling while keeping processing times fast and outputs high quality.


Our Solution


We built a FastAPI backend with optimized inference workflows and async processing. The React frontend provides a clean UI where users can upload photos, preview before and after results and download enhanced images.


Architecture Decisions


**PyTorch for AI models** gave us access to state of the art models for super resolution, face restoration and image enhancement. We optimized them for inference using TorchScript.


**FastAPI with async processing** ensures that multiple image enhancement requests can be handled concurrently without blocking. Long running tasks are processed in the background with progress updates via WebSockets.


**Redis for task queuing** manages the enhancement jobs. When a user uploads an image, it goes into a Redis queue and a worker processes it asynchronously.


Key Technical Highlights


  • 1. **Multiple enhancement types** - Face restoration, noise reduction, sharpening, color correction, upscaling
  • 2. **Before/after preview** - Side by side comparison before downloading
  • 3. **Batch processing** - Upload and enhance multiple images at once
  • 4. **History tracking** - Users can access their previously enhanced images

    • Results


    Processing time reduced by 70% compared to competitors with users reporting professional grade results. The platform is now processing thousands of images monthly with plans to scale further.