Papers with Code 아카이브
Blind Super-Resolution
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Blind Super-Resolution is an image processing technique that aims to reconstruct high-resolution images from low-resolution counterparts without prior knowledge of the degradation process.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xylgroup/mlmc
공식 코드Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
https://github.com/yuki-11/csbsr
공식 코드Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
https://github.com/zj-binxia/kdsr
공식 코드Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
https://github.com/zj-binxia/mrda
공식 코드Meta-Learning based Degradation Representation for Blind Super-Resolution
https://github.com/20151001860/Real_ESRGAN_paddle
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/BillOuyang/KernelGan-s3
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/JHLew/PerPix
https://github.com/JaguAroo/DASR
Unsupervised Degradation Representation Learning for Blind Super-Resolution
https://github.com/Lornatang/KernelGAN
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/Lornatang/SFTMD-PyTorch
https://github.com/NJU-PCALab/AddSR
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
https://github.com/RomanovIgnat/KernelGAN
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/TencentARC/FAIG
Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
https://github.com/algolzw/dcls
Deep Constrained Least Squares for Blind Image Super-Resolution
https://github.com/bayuudachi/Real-esr-gan
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/code-implementation1/Code3/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/final-0/Real-ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/final-0/Real-ESRGAN-bicubic
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/mehrhardt/blind_remote_sensing
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
https://github.com/naagar/art_restoration_dm
Exploiting Diffusion Prior for Real-World Image Super-Resolution
https://github.com/sberbank-ai/real-esrgan
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/sweetcocoa/IKC
https://github.com/yhjo09/AdaTarget
https://github.com/ysook1m/Enhanced_KernelGAN
https://github.com/yuanjunchai/IKC
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.