Readme
BSRGAN: Practical Image and Video Restoration 🖼️🎥
A deep learning model designed to improve low-quality images and videos by addressing real-world degradations like blur, noise, and compression artifacts.
What It Does
BSRGAN helps restore visual content by: - Enhancing resolution (2x or 4x upscaling) - Reducing blur and noise in old photos - Improving compressed/low-quality video frames - Handling unknown degradation types automatically
For videos: Processes each frame individually then reconstructs them into a coherent sequence
How It Works
Trained using a realistic degradation model that simulates: - Multiple blur types (motion, lens, sensor) - Mixed noise patterns (Gaussian + camera-specific) - Various downsampling methods - JPEG compression artifacts
Unlike ESRGAN and other GAN-based methods that assume ideal degradation scenarios, BSRGAN specifically addresses the unpredictable quality loss found in real-world images and footage.
Key Features
- Blind Restoration : Works without prior knowledge of image degradation
- Frame Consistency : Maintains temporal coherence when processing video
- Flexible Input : Accepts images (JPG/PNG) and videos (MP4/MOV)
- Quality Control : Adjustable parameters for noise reduction and sharpness
Common Use Cases
- Restoring historical photos/family albums
- Upscaling low-resolution surveillance footage
- Improving compressed social media content
- Pre-processing for archival digitization projects
- Enhancing video conference quality
Limitations
- Maximum resolution: 1536x1536 for images
- Recommended video length: <2 minutes
- Processing time varies by hardware (~10 sec/image on GPUs)
- May struggle with extreme degradation cases
Credits
Developed by researchers at ETH Zurich:
@inproceedings{zhang2021designing,
title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
booktitle={ICCV},
year={2021}
}
Original implementation:
cszn/BSRGAN on GitHub
Maintained by
@zsxkib
for Replicate integration
Note : Video processing works by applying the image model frame-by-frame. For optimal results, combine with temporal stabilization tools in post-processing.
