Papers with Code 아카이브
Super-Resolution
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DeokyunKim/Progressive-Face-Super-Resolution
공식 코드Progressive Face Super-Resolution via Attention to Facial Landmark
https://github.com/Ding-Liu/NLRN
공식 코드https://github.com/ESDALab/HyperDS_tgrs
공식 코드Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method
https://github.com/EdwardFerdian/4DFlowNet
공식 코드4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics
https://github.com/EdwardSmith1884/3D-Object-Super-Resolution
공식 코드Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
https://github.com/EdwardSmith1884/Multi-View-Silhouette-and-Depth-Decomposition-for-High-Resolution-3D-Object-Representation
공식 코드Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
https://github.com/ElementAI/HighRes-net
공식 코드HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
https://github.com/Elin24/PSPL
공식 코드https://github.com/EquationWalker/CATANet
공식 코드CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
https://github.com/EscVM/RAMS
공식 코드https://github.com/Eulring/Hybrid-Function-Sparse-Representation
공식 코드Hybrid Function Sparse Representation towards Image Super Resolution
https://github.com/FarihaHossain/SwinFSR
공식 코드https://github.com/GinZhu/RDST
공식 코드https://github.com/GreyCC/DTLS
공식 코드Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising Model
https://github.com/GuHuangAI/ADM-Public
공식 코드Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
https://github.com/GuYuanjie/DeepFusionPrior
공식 코드Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging
https://github.com/GuangluDong0728/SFD
공식 코드https://github.com/HDCVLab/EDFace-Celeb-1M
공식 코드EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
https://github.com/HPAI-BSC/MetH-baselines
공식 코드MetH: A family of high-resolution and variable-shape image challenges
https://github.com/HSE-asavchenko/face-emotion-recognition
공식 코드https://github.com/HWQuantum/HistNet
공식 코드Robust super-resolution depth imaging via a multi-feature fusion deep network
https://github.com/Hammour-steak/ECDB
공식 코드https://github.com/Hammour-steak/GOUB
공식 코드Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
https://github.com/HarleyHK/FARF
공식 코드https://github.com/HarleyHK/JMPF
공식 코드Joint Maximum Purity Forest with Application to Image Super-Resolution
https://github.com/HendrikMuenster/superResolution
공식 코드https://github.com/HengRuiZ/brelu
공식 코드Efficient Integer-Arithmetic-Only Convolutional Neural Networks
https://github.com/HideUnderBush/UI2I_via_StyleGAN2
공식 코드Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network
https://github.com/Holmes-Alan/ABPN
공식 코드Image Super-Resolution via Attention based Back Projection Networks
https://github.com/Holmes-Alan/HBPN
공식 코드Hierarchical Back Projection Network for Image Super-Resolution
https://github.com/Holmes-Alan/RefVAE
공식 코드Variational AutoEncoder for Reference based Image Super-Resolution
https://github.com/Holmes-Alan/dSRVAE
공식 코드Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
https://github.com/ICT-MIRACLE-lab/CTSpine1K
공식 코드CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
https://github.com/IMPLabUniPr/swin2-mose
공식 코드Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
https://github.com/IVIPLab/FDIWN
공식 코드Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.