Real-ESRGAN: Image and video restoration tool

PyTorch framework for blind super-resolution using GANs.

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Learn more about Real-ESRGAN

Real-ESRGAN is a PyTorch-based image restoration framework designed to handle multiple degradation types in a unified approach. The implementation uses neural network models trained exclusively on synthetic data to generalize across real-world image quality issues. It provides multiple model variants optimized for different use cases, including general image restoration and specialized anime content processing. The framework supports inference through multiple backends including PyTorch, NCNN with Vulkan acceleration, and cloud deployment options.

Synthetic Training Data

Models train exclusively on synthetically degraded images without requiring paired real-world datasets. Eliminates costly data collection while generalizing effectively to real image restoration scenarios.

Unified Degradation Handling

Single model addresses super-resolution, denoising, and JPEG artifact removal simultaneously. Replaces multiple specialized networks with one inference pass for combined restoration tasks.

Multi-Backend Deployment

Supports multiple inference backends including PyTorch, NCNN, and Vulkan for broad hardware compatibility. Run on NVIDIA GPUs with CUDA, AMD GPUs with Vulkan, or CPU-only environments, enabling deployment across diverse infrastructure setups.

from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
import cv2

model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
upsampler = RealESRGANer(scale=4, model_path='RealESRGAN_x4plus.pth', model=model)

img = cv2.imread('input.jpg', cv2.IMREAD_COLOR)
output, _ = upsampler.enhance(img, outscale=4)
cv2.imwrite('output.jpg', output)