Papers with Code 아카이브
highlight removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Highlight removal refers to the process of eliminating or reducing the presence of specular highlights in an image. Specular highlights are bright spots or reflections that occur when light reflects off a shiny or reflective surface, such as glass, metal, or oily skin. These highlights can often obscure or distort the underlying details of the image, making it difficult to analyze or process.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Dual-Hybrid Attention Network for Specular Highlight Removal
A Highlight Removal Method for Capsule Endoscopy Images
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Text-Aware Single Image Specular Highlight Removal
Polarization Guided Specular Reflection Separation
Fast and High Quality Highlight Removal from A Single Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.