https://gitlab.telecom-paris.fr/RING/MERLIN
공식 코드As if by magic: self-supervised training of deep despeckling networks with MERLIN
Papers with Code 아카이브
Image Restoration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 3
- 보존된 평가 기록
- 17
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://gitlab.telecom-paris.fr/RING/SAR-CNN
공식 코드https://gitlab.telecom-paris.fr/RING/SAR2SAR
공식 코드SAR2SAR: a semi-supervised despeckling algorithm for SAR images
https://github.com/12dmodel/camera_sim
Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
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-211/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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/2023-MindSpore-1/ms-code-214/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN
https://github.com/2023-MindSpore-4/Code-5/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/2023-MindSpore-4/Code-5/tree/main/MIMO-UNet
https://github.com/2023-MindSpore-4/Code1/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2023-MindSpore-4/Code10/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2024-MindSpore-1/Code4/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/2minkyulee/aesop-auto-encoded-supervision-for-perceptual-image-super-resolution
https://github.com/59Kkk/pytorch_Ucolor_lcy
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/AdneneBoumessouer/Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/LEGO-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AdneneBoumessouer/MVTec-Anomaly-Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/AliRoyat/NTIRE2021-IQA-MACS-Pytorch
https://github.com/Araxeus/PNG-Upscale
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/AureliePeng/Keras-WaveletTransform
https://github.com/BillOuyang/KernelGan-s3
Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/Build-Week-Spotify-Song-Suggester-5/Data-Science
https://github.com/COMP6248-Reproducability-Challenge/selfsupervised-denoising
https://github.com/CV-Reimplementation/Ucolor-Reimplementation
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/Capsar/DL-GenISP
https://github.com/ChungHanYang/Image-retoration-based-on-SRresnet
https://github.com/CoinCheung/MFM
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
https://github.com/DDCM-2025/ddcm-compressed-image-generation
Compressed Image Generation with Denoising Diffusion Codebook Models
https://github.com/DSL-FIQA/DSL-FIQA
https://github.com/DmitryUlyanov/deep-image-prior
https://github.com/GarrickZ2/Image-Denoising
CycleISP: Real Image Restoration via Improved Data Synthesis
https://github.com/Georg030/MRI-Image-Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/GinZhu/MIASSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/GitDT11/EDSR-pytorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.