Acknowledgments
Our AI Image Enhancer is powered by cutting-edge open-source AI models developed by leading researchers and organizations. We are deeply grateful to these contributors for their groundbreaking work and commitment to open science.
Built with 6 Open-Source AI Models
All models are used in compliance with their respective open-source licenses. Full attribution and license texts are provided below.
Real-ESRGAN
Version: x2plus, x4plus
Purpose
AI-powered image super-resolution for 2x, 4x, and 8x upscaling
Authors
Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan
Organization
Tencent ARC Lab
GFPGAN
Version: v1.3, v1.4
Purpose
Generative Facial Prior GAN for face restoration and enhancement
Authors
Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
Organization
Tencent ARC Lab
BiRefNet
Version: Latest
Purpose
Bilateral reference network for high-quality background removal
Authors
Zheng Peng
Organization
Independent Research
LaMa
Version: Resolution-robust Large Mask Inpainting
Purpose
Large mask inpainting for object removal and image restoration
Authors
Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, et al.
Organization
Samsung AI Center
NAFNet
Version: REDS deblurring model
Purpose
Nonlinear Activation Free Network for high-quality image deblurring
Authors
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, Jian Sun
Organization
Megvii Research
EasyOCR
Version: Latest
Purpose
Optical character recognition for text detection and extraction
Authors
Jaided AI Team
Organization
Jaided AI
Resources
If you use our service in your research or project, please consider citing the original papers of the AI models we use. This helps support the researchers and encourages continued innovation in open-source AI.