https://github.com/GuoShi28/GCP-Net
공식 코드Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
Papers with Code 아카이브
Image Restoration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Hammour-steak/ECDB
공식 코드https://github.com/Hammour-steak/GOUB
공식 코드Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
https://github.com/Harry-Thasarathan/TCVC
공식 코드https://github.com/HilaManor/GaussianDenoisingPosterior
공식 코드On the Posterior Distribution in Denoising: Application to Uncertainty Quantification
https://github.com/IIGROUP/RADN
공식 코드Region-Adaptive Deformable Network for Image Quality Assessment
https://github.com/IceClear/StableSR
공식 코드Exploiting Diffusion Prior for Real-World Image Super-Resolution
https://github.com/JWSoh/VDIR
공식 코드https://github.com/Jae-Moo/RGM
공식 코드https://github.com/Jiang-maomao/flare-removal
공식 코드MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal
https://github.com/JianghaiSCU/Diffusion-Low-Light
공식 코드Low-Light Image Enhancement with Wavelet-based Diffusion Models
https://github.com/JingyunLiang/FKP
공식 코드Flow-based Kernel Prior with Application to Blind Super-Resolution
https://github.com/JunlinHan/CWR
공식 코드https://github.com/KU-CVLAB/Perturbed-Attention-Guidance
공식 코드Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
https://github.com/Kaiwen-Zhu/AgenticIR
공식 코드An Intelligent Agentic System for Complex Image Restoration Problems
https://github.com/KumapowerLIU/PD-GAN
공식 코드https://github.com/KupynOrest/DeblurGANv2
공식 코드DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
https://github.com/LimBee/NTIRE2017
공식 코드Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LintaoPeng/U-shape_Transformer
공식 코드https://github.com/LituRout/OptimalTransportModeling
공식 코드https://github.com/LongguangWang/DASR
공식 코드Unsupervised Degradation Representation Learning for Blind Super-Resolution
https://github.com/Lotayou/Face-Renovation
공식 코드HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/MC-E/COLA-Net
공식 코드COLA-Net: Collaborative Attention Network for Image Restoration
https://github.com/MKFMIKU/D2SM
공식 코드https://github.com/MathFLDS/MRAugment
공식 코드Data augmentation for deep learning based accelerated MRI reconstruction with limited data
https://github.com/Mdraqibkhan/Phaseformer
공식 코드Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond
https://github.com/Mehran-Soltani/ChannelNet
공식 코드https://github.com/Microtreei/TSD-SR
공식 코드TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
https://github.com/Mnster00/CasualSR
공식 코드CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference
https://github.com/Mosasaur5526/BCM-iCT-torch
공식 코드https://github.com/NVlabs/PL4NN
공식 코드https://github.com/NVlabs/noise2noise
공식 코드https://github.com/QihanZhao/enlighten-anything
공식 코드Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
https://github.com/RK621/Unsupervised-Restoration-PLD
공식 코드Unsupervised Image Restoration Using Partially Linear Denoisers
https://github.com/RyanXingQL/RBQE
공식 코드Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
https://github.com/SHI-Labs/Pyramid-Attention-Networks
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.