upscalers

Upscalers are software or hardware systems that take low-resolution media and generate higher-resolution versions by adding new pixels. The goal is to improve perceived detail and viewing quality without simply magnifying existing pixels.

Upscalers cover a range of techniques, from simple resampling methods such as nearest-neighbor, bilinear, and bicubic interpolation to advanced supervised learning approaches. Modern upscalers often use convolutional neural networks or generative adversarial networks that attempt to reconstruct plausible high-frequency details, textures, and edges. Some systems combine traditional interpolation with learned priors to balance sharpness and artifact suppression.

Common applications include televisions and streaming devices that upscale received video, video game consoles that scale rendered frames for different displays, and photo editors or professional imaging workflows that enhance image resolution. Real-time upscaling is also used in mobile devices and professional pipelines to accommodate display or storage constraints.

Performance is typically evaluated with objective metrics such as PSNR and SSIM, but perceptual quality and user experience are important considerations. Different algorithms may produce sharper edges yet introduce artifacts or inconsistent texture, and temporal coherence can be a challenge for video upscaling.

Limitations include potential artifacts such as ringing or halos, the risk of texture hallucination, and high computational demands for real-time processing. Ongoing research aims to improve realism, reduce artifacts, and enable efficient on-device operation. Notable implementations span traditional interpolation methods, deep learning models like ESRGAN and Real-ESRGAN, upscaling tools in waifu2x and SRCNN, and hardware-accelerated upscaling in modern displays and GPUs.