AI Enhancer
Coming SoonAI Enhancer
即将推出
基于 Real-ESRGAN 的 AI 超分辨率技术，智能提升视频画质，让模糊视频焕然一新。
功能开发中
敬请期待
How to Use
- Click the area above to select a file, or drag and drop a file onto the page
- Adjust parameters in the settings area
- Click the process button and download the result when ready
Frequently Asked Questions
How It Works
The AI Enhancer uses ONNX Runtime WebAssembly to run neural network models directly in the browser for super-resolution upscaling. The model architecture is based on a lightweight CNN (Convolutional Neural Network) optimized for real-time inference in WebAssembly.
The process works in tiles: the input video frame is divided into overlapping patches (typically 64×64 pixels), each patch is passed through the neural network which predicts the high-resolution version, and the output patches are stitched back together with blending to avoid seam artifacts. The network uses sub-pixel convolution layers to achieve 2x or 4x spatial upscaling.
The ONNX Runtime WebAssembly backend leverages SIMD (Single Instruction, Multiple Data) instructions for accelerated matrix operations. The model weights (typically 2-10MB) are loaded once and cached for subsequent frames. Processing runs on a dedicated Web Worker with progress reporting per frame.
Tips & Best Practices
- 2x upscaling is recommended for most content — it doubles resolution while maintaining natural-looking detail. 4x may introduce artifacts on some content.
- Best results with lower resolution source: AI enhancement adds the most value to 480p/720p content being upscaled to 1080p/4K.
- Processing time: AI enhancement is CPU-intensive. Expect 5-15x slower than real-time playback speed.
- Content type matters: Works best on faces, text, and geometric shapes. Less effective on very noisy or heavily compressed source material.
- Try before committing: Process a single frame first to evaluate the enhancement quality before processing the entire video.
- Combine with compression: Upscale first, then apply gentle compression to manage the larger output file size.
Use Cases
Content creators upscaling older 720p video content to 1080p or 4K for modern high-resolution displays and YouTube uploads.
Archivists restoring and enhancing vintage video footage to improve clarity for digital preservation. Security teams upscaling low-resolution surveillance footage to improve detail visibility for analysis. Educational institutions enhancing legacy course recordings to meet modern video quality standards. Film restoration enthusiasts improving the resolution of classic films for personal enjoyment. Real estate photographers enhancing property video tours to look sharper on high-resolution listing platforms.