upscalingteknikker

Upscaling techniques (often referred to as upscalingteknikker in Danish) encompass a set of methods for increasing the apparent resolution of digital images, video, or other raster media by generating new pixel data. The goal is to reproduce more detail than the original sample while minimizing artifacts. They are used in photography, video streaming, printing, and surveillance, and range from simple interpolation to sophisticated neural networks.

Classical approaches rely on interpolation: nearest-neighbor, bilinear, bicubic, and Lanczos resampling. These methods extend existing pixels but may blur edges or introduce ringing when scaling aggressively. Edge-preserving and content-aware interpolations attempt to maintain sharpness and reduce artifacts but still rely on original information.

Modern methods employ deep learning-based super-resolution, using neural networks trained on pairs of low- and high-resolution images. They can reconstruct plausible textures and edges, often outperforming traditional methods when ample training data and compute are available. However, they may hallucinate details and introduce artifacts if misused or biased by training data.

Applications include consumer photography, television and streaming, printing, surveillance, and scientific imaging. Practical use involves balancing accuracy, speed, and resource consumption. Evaluation often relies on metrics such as PSNR and SSIM, complemented by perceptual tests and domain-specific criteria. Tools range from open-source libraries to commercial software, and progress continues with multi-frame and diffusion-based approaches.