Starting with Simple CNNs
My journey began with simple Convolutional Neural Networks (CNNs), using straightforward neural networks to restore facial images. These early experiments helped me understand how convolutional layers could be applied to improve clarity and detail. Although the results were promising, I quickly realized that more complex models would be needed to achieve higher-quality enhancements.
Experimenting with GFPGAN and CodeFormer
As I continued my research, I moved on to experimenting with more advanced models like GFPGAN and CodeFormer. These models brought significant improvements, allowing for enhanced facial features while preserving natural expressions. They were particularly good at restoring details like eyes and mouth regions, which are critical for realistic face enhancements. These experiments expanded my toolkit and gave me a deeper understanding of facial restoration.
Building an Ultra-Efficient Model
The real breakthrough came when I designed my own ultra-efficient face enhancement model. Optimized for 512x512 image sizes, my model achieved high-quality results while being incredibly lightweight—only 80 MB, compared to much larger models. This level of efficiency allows for real-time face enhancements on a wide range of devices, even mobile platforms, without sacrificing image quality. I'm proud that my model can transform low-resolution images into high-definition visuals quickly and efficiently. This innovative solution has been integrated by companies, enabling them to enhance their digital media and improve user engagement seamlessly.
