Image Upscaler
Based on advanced ESRGAN deep learning models, intelligently upscales image resolution while preserving details and reducing blur and distortion. Suitable for photo restoration, design material processing, and similar scenarios.
Key Features
Smart Upscaling Algorithm
Uses ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) technology through deep learning models to reconstruct image details for high-quality upscaling.
Multiple Model Options
- ESRGAN Medium: Balances speed and quality, suitable for most scenarios
- ESRGAN Thick: Best quality with richer detail restoration, longer processing time
Flexible Scaling Factors
- 2x Upscaling: Suitable for light resolution enhancement needs
- 4x Upscaling: Suitable for major resolution boost, converting low-res to high-res images
Advanced Parameter Adjustment
- Tile Size: Adjust processing tile size (16-64); smaller values reduce UI blocking but increase processing time
- Padding: Set tile edge padding (0-10); minimum recommended value 3 to prevent tile seam artifacts
How to Use
- Upload image needing upscaling
- Select model (Medium or Thick) based on quality and speed needs
- Set scaling factor (2x or 4x)
- Adjust parameters in settings panel (optional)
- Click "Upscale" button to start processing
- View before/after comparison (drag slider)
- Download result (PNG format preserving best quality)
Application Scenarios
- Photo restoration (upscale old/low-res photos to high definition)
- Design materials (upscale low-res assets, icons, logos for high-res design needs)
- Social media (upscale blurry images for better visual effect and reach)
- Game beautification (upscale game screenshots and assets)
Important Notes
- Supported formats: JPG, JPEG, PNG, HEIC, WEBP
- Recommended image size max 4096×4096 pixels; oversized may fail
- 4x upscaling increases image size 16 times; note device performance and memory
- Processing time depends on image size, scaling factor, model choice; large images may take minutes
- Upscaling effect depends on original quality; overly blurry or damaged originals may have poor results
Technical Implementation
Uses ESRGAN deep learning models trained through Generative Adversarial Networks (GAN) to intelligently reconstruct image textures and details. Models run browser-side protecting user privacy; all processing completes locally. For efficiency, uses tile processing strategy splitting large images into small blocks processed individually then seamlessly stitched.
