https://github.com/seungjun45/Water-Filling
공식 코드Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
Papers with Code 아카이브
Shadow Removal
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Image Shadow Removal
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/storymy/take-off-eyeglasses
공식 코드Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
https://github.com/tsingqguo/exposure-fusion-shadow-removal
공식 코드https://github.com/tsingqguo/inpaint4shadow
공식 코드https://github.com/vinthony/ghost-free-shadow-removal
공식 코드Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
https://github.com/xinrui014/softshadow
공식 코드SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
https://github.com/xuyimin0926/sadc
공식 코드https://github.com/yingqinghe/shadow-removal-via-generative-priors
공식 코드https://github.com/zhangbaijin/sam-helps-shadow
공식 코드SAM-helps-Shadow:When Segment Anything Model meet shadow removal
https://github.com/zhangbaijin/spa-former-shadow-removal
공식 코드SpA-Former: Transformer image shadow detection and removal via spatial attention
https://github.com/zhangbaijin/spatial-transformer-shadow-removal
공식 코드SpA-Former: Transformer image shadow detection and removal via spatial attention
https://github.com/zhling2020/RIS-GAN
공식 코드RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
https://github.com/zipei-chen/canet
공식 코드https://github.com/zysxmu/hqss
공식 코드https://github.com/CV-Reimplementation/AEFNet-Reimplementation
https://github.com/CV-Reimplementation/BMNet-Reimplementation
https://github.com/CV-Reimplementation/DHAN-reimplementation
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
https://github.com/CV-Reimplementation/TraditionalDocumentShadowRemoval
Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android
https://github.com/HiveYuan/ShadowRemoval-RIS-GAN_self_implement
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
https://github.com/IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks
https://github.com/KevinJ-Huang/BMNet
https://github.com/Param-Raval/ipro
https://github.com/Param-Raval/shadow-sight
https://github.com/fabio-sim/DocShadow-ONNX-TensorRT
https://github.com/hangong/deshadow
Interactive Removal and Ground Truth for Difficult Shadow Scenes
https://github.com/jiaruixu/st-cgan
https://github.com/jiexiaou/homoformer
https://github.com/jinwan1994/SG-ShadowNet
https://github.com/kjybinp/SCGAN
https://github.com/mducducd/Shadow-Removal
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
https://github.com/mducducd/ghost-free-shadow-removal
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
https://github.com/nachifur/DMTN
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
https://github.com/ppak1991cy/RIS-GAN
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
https://github.com/sreenivas88/LP-IOANet
LP-IOANet: Efficient High Resolution Document Shadow Removal
https://github.com/stevewongv/dsc-pytorch
Direction-aware Spatial Context Features for Shadow Detection and Removal
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.