Upsampling
The process of increasing the resolution of an image or signal by adding pixels and estimating missing information through interpolation or deep learning techniques.
Upsampling refers to increasing the pixel count of an image beyond its original resolution. When scaling a 256x256 image to 1024x1024, approximately 93.75% of pixel data must be synthesized through estimation.
- Nearest-neighbor: Copies the closest pixel value. Produces blocky artifacts but preserves hard edges, suited to pixel art. Negligible computational cost
- Bilinear: Weighted average of four nearest pixels. Smooth results but blurs edges
- Bicubic: Cubic polynomial fitted to 16 surrounding pixels, balancing sharpness and smoothness
- Lanczos: Windowed sinc filter maintaining sharpness
Deep learning super-resolution models (SRCNN, ESRGAN, Real-ESRGAN) learn high-frequency patterns from training data and generate estimated textures and edges.
Example choices: bilinear for real-time rendering, bicubic or Lanczos for print, and AI super-resolution when quality takes priority.