超解像と画像復元
小さくぼやけた古い画像を鮮明にする
本ページの本文は英語で提供されています。タイトルと導入は日本語化されています。
この能力とは何か
Takes a low-resolution image or a degraded one — blurry, noisy, scratched — and outputs a higher-resolution or cleaner version. It is not mere upscaling: it fills high-frequency detail from learned image priors, and precisely because it fills, some of it is guesswork. Unlike image-to-image the goal is restoration, not stylisation.
技術的にどう実現するか
The classic route regresses high-resolution pixels with convolutional networks and leans on perceptual and adversarial losses for crisper texture, trading pixel fidelity for realism. Diffusion models were later adopted to fill detail from a generative prior, doing better on severe degradation. Because real-world degradation is complex and unknown, training often randomises the degradation so the model does not depend on one fixed downsampling.
代表的な製品
4関連する組織
代表的な用途
- Restoring old photos and family footage
- Detail enhancement for surveillance and satellite imagery
- Sharpening medical and microscopy images
- Upscaling assets for print and publishing
どう評価するか
- PSNR
- Peak signal-to-noise ratio, a pixel-level fidelity measure
- SSIM
- Structural similarity, closer to human judgement than PSNR
- LPIPS
- Perceptual distance, judging naturalness of texture
限界と難しさ
- It invents texture that was never there; enlarged faces and text can become something else entirely
- High PSNR does not mean it looks good; over-smoothed outputs score well yet lack detail
- Generalisation is poor on degradation types unseen in training, such as a particular compression artefact