Papers with Code 아카이브

Image Super-Resolution

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
5
평가표
69
보존된 평가 기록
748

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

평가 결과

데이터셋과 평가 조건을 선택해 성능·논문·코드를 비교하세요. 보존된 Papers with Code 기록으로, 최신 순위는 아닙니다.

Middlebury - 2x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

Middlebury - 4x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

OLI2MSI - 3x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution / Multispectral Image Super-resolution

평가 기록 1건

Sen2venus - 4x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution / Multispectral Image Super-resolution

평가 기록 1건

Set14

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

Set5 - 5x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

Set5 - 6x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

ShipSpotting

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

TextZoom

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

Urban100 - 16x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

USR-248 - 4x upscaling

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

WLFW

원본 태스크: 3D Object Super-Resolution / Super-Resolution / Image Super-Resolution

평가 기록 1건

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.