Papers with Code 아카이브

Sensor Modeling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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<span style="color:grey; opacity: 0.6">( Image credit: LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving )</span>

기법

관련 논문에서 소개한 기법입니다. 이 태스크에서의 평가 여부는 평가 결과에서 확인하세요.

DeepIR

DeepIR, or Deep InfraRed image processing, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a sequence of images into the scene-dependent radiant flux, and a slowly changing scene-independent non-uniformity. DeepIR combines the physics of microbolometer sensors, with powerful regularization capabilities by neural network-based representations. DeepIR relies on the key observation that jittering a camera, while unwanted in visible domain, is highly desirable in the thermal domain as it allows an accurate separation of the sensor-specific non-uniformities from the scene’s radiant flux.

Thermal Image Processing via Physics-Inspired Deep Networks

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