Image Super-Resolution
Enhance low-resolution images using advanced upscaling algorithms. Increase image size 2x-4x while preserving quality with Bicubic, Lanczos, or AI-enhanced methods.
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About Image Super-Resolution
- Bicubic: Classic interpolation using 4x4 pixel neighborhood. Fast and smooth.
- Lanczos: High-quality resampling with sinc function. Preserves edges better.
- AI Enhancement: Edge-aware sharpening with gradient detection. Best quality.
- 100% Client-Side: All processing happens in your browser. Your images never leave your device.
Key Features
- Multiple Algorithms: Choose from Bicubic, Lanczos, or AI enhancement
- Flexible Scaling: 2x, 3x, or 4x upscaling factors
- Interactive Comparison: Before/after slider to see improvements
- Privacy First: All processing happens locally in your browser
- Download Results: Save upscaled images directly to your device
Technical Implementation
Upscaling Algorithms
- Bicubic interpolation with 4x4 kernel
- Lanczos resampling with sinc function
- AI enhancement: Sobel edge detection + adaptive sharpening
- Per-pixel processing with color channel separation
Technology Stack
- TypeScript for type-safe implementation
- HTML5 Canvas API for image manipulation
- Custom interpolation kernels
- Real-time processing with ImageData API
Performance
- Processes images in 100-500ms (depending on size)
- Client-side processing (no server upload)
- Optimized pixel-level operations
- Memory-efficient ImageData handling
Algorithm Comparison
Bicubic Interpolation
Classic method using weighted average of 16 nearest pixels (4x4 neighborhood).
Lanczos Resampling
Advanced sinc-based interpolation that preserves edges and fine details better.
AI Enhancement
Edge-aware sharpening with Sobel gradient detection for adaptive enhancement.
Real-World Applications
Professional Use:
- 📸Photography: Upscale old photos or enlarge images for printing
- 🎨Graphic Design: Enhance low-res assets for high-DPI displays
- 🛍️E-commerce: Improve product image quality from suppliers
- 🎬Video Production: Upscale video frames for 4K/8K content
Technical Benefits:
- ✓No quality loss: Better than simple resizing
- ✓Edge preservation: Maintains sharpness and details
- ✓Artifact reduction: Minimizes blur and aliasing
- ✓Privacy-safe: No cloud upload, everything local
How Super-Resolution Works
Step 1: Image Loading
The image is loaded into an HTML5 Canvas and converted to ImageData, providing direct pixel access for manipulation.
Step 2: Interpolation
For each new pixel, the algorithm calculates values based on surrounding pixels using weighted kernels (bicubic, Lanczos, or custom).
Step 3: Edge Enhancement (AI Mode)
Sobel operators detect edges, then adaptive sharpening is applied based on gradient strength to preserve detail without over-sharpening.
Step 4: Output Generation
The enhanced ImageData is rendered back to Canvas and converted to a downloadable PNG with preserved quality.
Current Limitations & Future Enhancements
Current Approach:
- Traditional interpolation methods
- Works well for moderate upscaling (2x-4x)
- Processing time increases with image size
- Limited to basic edge enhancement
Future with Deep Learning:
- ESPCN, SRGAN, or Real-ESRGAN models
- Better texture reconstruction
- Face/text-aware enhancement
- WebGL/WebGPU acceleration
This demo uses classical computer vision algorithms. For production applications, I can implement state-of-the-art deep learning models for superior results.