https://gitee.com/HarveyYeung/PUGAN_MindSpore.git
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Underwater image enhancement is a technique used to improve the quality of underwater images. Due to the unique properties of the underwater environment, images captured underwater often suffer from degradation caused by absorption and scattering of light. These effects can result in low contrast, blurred images with a dominant blue or green color cast. Enhancement techniques aim to correct these issues and improve the visibility within the image. These methods can include color correction to remove the color cast, contrast enhancement to improve the visibility of underwater objects, and dehazing techniques to reduce the scattering effect. These enhancements are crucial in various applications, including underwater exploration, marine biology research, underwater archaeology, and in improving the performance of underwater vision systems used in autonomous underwater vehicles.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.