초해상도와 이미지 복원
작고 흐린 오래된 이미지를 선명하게 만든다
이 페이지의 본문은 영어로 제공됩니다. 제목과 요약은 한국어로 번역되었습니다.
이 능력이 뜻하는 것
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.
기술적으로 구현하는 방법
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.
대표 제품
4관련 기관
대표적 용도
- Restoring old photos and family footage
- Detail enhancement for surveillance and satellite imagery
- Sharpening medical and microscopy images
- Upscaling assets for print and publishing
성능을 평가하는 방법
- 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
경계와 난점
- 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