https://github.com/ICT-MIRACLE-lab/CTSpine1K
공식 코드CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
Papers with Code 아카이브
Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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
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/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/JingyunLiang/FKP
공식 코드Flow-based Kernel Prior with Application to Blind Super-Resolution
https://github.com/KyanChen/FunSR
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/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/Lotayou/Face-Renovation
공식 코드HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://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
https://github.com/MKFMIKU/SrSENet
공식 코드An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
https://github.com/Maclory/SPSR
공식 코드Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/Madhu081096/Reference-based-MRI-superresolution-using-texture-transfer
공식 코드Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
https://github.com/MandyZChen/srez
공식 코드https://github.com/ManuelFritsche/real-world-sr
공식 코드https://github.com/MclTTI/RainScaleGAN
공식 코드RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
https://github.com/Mehran-Soltani/ChannelNet
공식 코드https://github.com/Microtreei/TSD-SR
공식 코드TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
https://github.com/NJU-Jet/SR_Mobile_Quantization
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.