Papers with Code 아카이브
Rain Removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://bitbucket.org/aauvap/rainremoval
공식 코드https://github.com/12138-chr/mtair
공식 코드Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
https://github.com/Aitical/MCLIR
공식 코드Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
https://github.com/Algolzw/FoD
공식 코드https://github.com/Ascend-Research/Turtle
공식 코드Learning Truncated Causal History Model for Video Restoration
https://github.com/Ephemeral182/UDR-S2Former_deraining
공식 코드https://github.com/GuangluDong0728/CSUD-Unsupervised-Deraining-CVPR2025
공식 코드Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining
https://github.com/HDCVLab/Enhanced-Spatio-Temporal-Interaction-Learning-for-Video-Deraining
공식 코드Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
https://github.com/Hammour-steak/ECDB
공식 코드https://github.com/Hammour-steak/GOUB
공식 코드Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
https://github.com/JunlinHan/BID
공식 코드https://github.com/LI-Hao-SJTU/DerainRLNet
공식 코드Robust Representation Learning with Feedback for Single Image Deraining
https://github.com/OaDsis/DerainCycleGAN
공식 코드DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
https://github.com/Schizophreni/MSResNet-TRNR
공식 코드TRNR: Task-Driven Image Rain and Noise Removal with a Few Images Based on Patch Analysis
https://github.com/ShenZheng2000/SAPNet-for-image-deraining
공식 코드SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining
https://github.com/ShugoYamashita/DFSSM
공식 코드https://github.com/Snowfallingplum/rain-streaks-removal
공식 코드Directional Regularized Tensor Modeling for Video Rain Streaks Removal
https://github.com/SumiHui/MENET
공식 코드https://github.com/Talented-Q/Restorer
공식 코드Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance
https://github.com/Tombs98/CAPTNet
공식 코드Prompt-based Ingredient-Oriented All-in-One Image Restoration
https://github.com/Tombs98/M3SNet
공식 코드A Mountain-Shaped Single-Stage Network for Accurate Image Restoration
https://github.com/TonyHongtaoWu/RainMamba
공식 코드RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
https://github.com/UCLA-VMG/GT-RAIN
공식 코드Not Just Streaks: Towards Ground Truth for Single Image Deraining
https://github.com/Vinayak-VG/GAURA
공식 코드GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary Views
https://github.com/Yingjun-Du/VID
공식 코드https://github.com/ZhendongWang6/Uformer
공식 코드Uformer: A General U-Shaped Transformer for Image Restoration
https://github.com/aitical/dswinir
공식 코드https://github.com/akshaydudhane16/dynet
공식 코드Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration
https://github.com/algolzw/daclip-uir
공식 코드Controlling Vision-Language Models for Multi-Task Image Restoration
https://github.com/algolzw/image-restoration-sde
공식 코드Image Restoration with Mean-Reverting Stochastic Differential Equations
https://github.com/alphacatplus/vmambair
공식 코드https://github.com/asleepytree/freqmamba
공식 코드FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining
https://github.com/booker-max/egvd
공식 코드https://github.com/c-yn/adair
공식 코드AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
https://github.com/cctakaet/contourletnet-bmvc2021
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.