Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/rwightman/pytorch-image-models
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/ryanlu2240/boosting-diffusion-guidance-via-learning-degradation-aware-models-for-blind-super-resolution
공식 코드Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
https://github.com/ryanlu2240/dadiff
공식 코드Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
https://github.com/ryanxingql/winner-ntire22-vqe
공식 코드Progressive Training of A Two-Stage Framework for Video Restoration
https://github.com/ryeoat3/gomin
공식 코드https://github.com/s4nyam/panodiff
공식 코드PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
https://github.com/saber-zero/hyperfusion
공식 코드https://github.com/sad192/large-kernel-transformer
공식 코드LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
https://github.com/saeed-anwar/SRsurvey
공식 코드https://github.com/saic-mdal/HiDT
공식 코드https://github.com/samuro95/gspnp
공식 코드https://github.com/samuro95/prox-pnp
공식 코드Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
https://github.com/sanechips-multimedia/syenet
공식 코드https://github.com/sanghyun-son/srwarp
공식 코드SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation
https://github.com/sangjun7/lpae
공식 코드https://github.com/sangyun884/mssr
공식 코드https://github.com/sascha-kirch/rgb-d-fusion
공식 코드RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects
https://github.com/saturnian77/asid
공식 코드https://github.com/saturnian77/drsan
공식 코드A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution
https://github.com/sbelharbi/sr-caco-2
공식 코드SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
https://github.com/scaomath/torch-cfd
공식 코드Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows
https://github.com/sci-sjj/EDSRmodelling
공식 코드Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling
https://github.com/sczhou/IGNN
공식 코드Cross-Scale Internal Graph Neural Network for Image Super-Resolution
https://github.com/sdl-asu/acla
공식 코드https://github.com/sdv4/FDPL
공식 코드https://github.com/sedaboni/DeepCEL0
공식 코드DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy
https://github.com/sefibk/KernelGAN
공식 코드Blind Super-Resolution Kernel Estimation using an Internal-GAN
https://github.com/sehoffmann/atmodist
공식 코드https://github.com/sejinpark-cv/im-lut
공식 코드IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
https://github.com/seungho-snu/SROOE
공식 코드Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
https://github.com/seungho-snu/fxsr
공식 코드Flexible Style Image Super-Resolution using Conditional Objective
https://github.com/sfm-sr-denoising/sfm
공식 코드Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
https://github.com/sh-lee-prml/hierspeechpp
공식 코드https://github.com/shadowbbbb/depthor
공식 코드DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image
https://github.com/shadyabh/Correction-Filter
공식 코드Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.