https://github.com/IVRL/w2s
공식 코드W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping
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/IceClear/StableSR
공식 코드Exploiting Diffusion Prior for Real-World Image Super-Resolution
https://github.com/JNNNNYao/LINF
공식 코드Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
https://github.com/JWSoh/MZSR
공식 코드https://github.com/JWSoh/NatSR
공식 코드Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination
https://github.com/JWSoh/VDIR
공식 코드https://github.com/JaeYeonKang/STVUN-Pytorch
공식 코드https://github.com/Jakaria08/Filter_Enhance_Detect
공식 코드https://github.com/JalaliLabUCLA/Jalali-Lab-Implementation-of-RAISR
공식 코드Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning
https://github.com/JasonBoy1/TextZoom
공식 코드https://github.com/Jee-King/TSAN
공식 코드A Two-Stage Attentive Network for Single Image Super-Resolution
https://github.com/Jia-Research-Lab/Simple-SR
공식 코드https://github.com/JiahuiYu/slimmable_networks
공식 코드Universally Slimmable Networks and Improved Training Techniques
https://github.com/JiahuiYu/wdsr_ntire2018
공식 코드Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/Jiamian-Wang/IST_for_SR_pruning
공식 코드Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
https://github.com/JihyongOh/FISR
공식 코드FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
https://github.com/JihyongOh/JSI-GAN
공식 코드https://github.com/JingyunLiang/FKP
공식 코드Flow-based Kernel Prior with Application to Blind Super-Resolution
https://github.com/Joker-ZXR/TSSC-Net
공식 코드https://github.com/KellyYutongHe/satellite-pixel-synthesis-pytorch
공식 코드Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
https://github.com/KyanChen/FunSR
공식 코드https://github.com/LabForComputationalVision/universal_inverse_problem
공식 코드Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
https://github.com/Lattle-y/AI-recognition-for-lq-ed
공식 코드https://github.com/LeeHW-THU/A-LIIF
공식 코드Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution
https://github.com/LeiaLi/SRDiff
공식 코드SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
https://github.com/LimBee/NTIRE2017
공식 코드Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LongguangWang/DASR
공식 코드Unsupervised Degradation Representation Learning for Blind Super-Resolution
https://github.com/LongguangWang/PAM
공식 코드Parallax Attention for Unsupervised Stereo Correspondence Learning
https://github.com/LongguangWang/PASSRnet
공식 코드Learning Parallax Attention for Stereo Image Super-Resolution
https://github.com/LongguangWang/SMSR
공식 코드Exploring Sparsity in Image Super-Resolution for Efficient Inference
https://github.com/LongguangWang/SOF-VSR
공식 코드Deep Video Super-Resolution using HR Optical Flow Estimation
https://github.com/LongguangWang/SOF-VSR-Super-Resolving-Optical-Flow-for-Video-Super-Resolution-
공식 코드Learning for Video Super-Resolution through HR Optical Flow Estimation
https://github.com/Lotayou/Face-Renovation
공식 코드HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/Lxiangyue/GenN2N
공식 코드https://github.com/MIVRC/MDCN-PyTorch
공식 코드MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
https://github.com/MIVRC/SSRDEFNet-PyTorch
공식 코드Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.