https://github.com/hust512/Tensor-GAN
공식 코드TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
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/huzexi/SADenseNet
공식 코드Efficient Light Field Reconstruction via Spatio-Angular Dense Network
https://github.com/hvcl/inr-diffusion
공식 코드https://github.com/hvision-nku/controlsr
공식 코드ControlSR: Taming Diffusion Models for Consistent Real-World Image Super Resolution
https://github.com/hvision-nku/srformer
공식 코드SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
https://github.com/hyeonjeong1/22-NeurIPS-Weather4Cast
공식 코드Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
https://github.com/hyeonjeong1/22-neurips-competition-baseline
공식 코드Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
https://github.com/hyhsimon/boosted-vsr
공식 코드Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
https://github.com/hyn2028/tpdm
공식 코드Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models
https://github.com/hyt35/prox-qn
공식 코드https://github.com/i2-multimedia-lab/cdformer
공식 코드CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution
https://github.com/i2-multimedia-lab/dsat
공식 코드Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
https://github.com/iamlab-ryerson/mlp-srgan
공식 코드MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer
https://github.com/ian-pang/supercalo
공식 코드https://github.com/ian0926/gdnet
공식 코드Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution
https://github.com/iandobbie/CUDA_SIMrecon
공식 코드Structured illumination microscopy image reconstruction algorithm
https://github.com/ianyeung/mgld-vsr
공식 코드Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
https://github.com/ibm/multimodal-3d-image-segmentation
공식 코드FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator
https://github.com/ibrahimethemhamamci/generatect
공식 코드GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
https://github.com/icandle/MAN
공식 코드Multi-scale Attention Network for Single Image Super-Resolution
https://github.com/icandle/camixersr
공식 코드https://github.com/icandle/ddistill-sr
공식 코드DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
https://github.com/icandle/efdn
공식 코드Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
https://github.com/icandle/plainusr
공식 코드PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution
https://github.com/icey-zhang/SuperYOLO
공식 코드SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
https://github.com/icon-lab/TranSMS
공식 코드TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
https://github.com/idearibosome/tf-bsrn-sr
공식 코드Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
https://github.com/idiosyncracies/Sebica
공식 코드Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network
https://github.com/ifnspaml/superresolutionmultiscaletraining
공식 코드A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
https://github.com/ifryed/HyperRes
공식 코드https://github.com/ihubdata-mobility/public-fgvd
공식 코드A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
https://github.com/imalne/pbasr
공식 코드A New Dataset and Framework for Real-World Blurred Images Super-Resolution
https://github.com/imatge-upc/3D-GAN-superresolution
공식 코드Brain MRI super-resolution using 3D generative adversarial networks
https://github.com/imt-project-lts-sr/mrunet-for-modis-super-resolution
공식 코드Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
https://github.com/indigopurple/efenet
공식 코드EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
https://github.com/inria-thoth/ddm4ip
공식 코드Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.