Papers with Code 아카이브
3D Object Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SSAW14/BeyondtheSpectrum
공식 코드https://github.com/SaharAlmahfouzNasser/MRI-SuperResolution
공식 코드https://github.com/Sangluisme/MultiviewLightEnhancedDepthSR
공식 코드https://github.com/Shanks0465/Localized_SuperResolution_Foreground
공식 코드Localized Super Resolution for Foreground Images using U-Net and MR-CNN
https://github.com/ShawkhIbneRashid/medical-images-sr
공식 코드Single MR Image Super-Resolution using Generative Adversarial Network
https://github.com/Sheldon04/CABM-pytorch
공식 코드CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
https://github.com/SherifAbdulatif/CMGAN
공식 코드CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
https://github.com/ShinyWang33/eSL-Net
공식 코드https://github.com/Shorya-Sharma/Game-Theory-For-Adversarial-Attacks-And-Defenses
공식 코드https://github.com/ShreyasSkandanS/DFuseNet
공식 코드DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
https://github.com/ShuhangGu/DASR
공식 코드Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training
https://github.com/Sindhu-Hegde/video-super-resolver
공식 코드Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors
https://github.com/StanislasDucotterd/Smooth-Plus-Sparse-Model
공식 코드Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction
https://github.com/Sudo-Biao/s-LWSR
공식 코드https://github.com/SunYM2020/UA-CMDet
공식 코드Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
https://github.com/Sungwon-Han/READ
공식 코드Lightweight and Robust Representation of Economic Scales from Satellite Imagery
https://github.com/TAMU-VITA/AGD
공식 코드AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/THUDM/CogView
공식 코드CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/TanakitInt/SRCNN-anime
공식 코드Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
https://github.com/Tang1705/Semantic-Lens-AAAI24
공식 코드Semantic Lens: Instance-Centric Semantic Alignment for Video Super-Resolution
https://github.com/TencentARC/GFPGAN
공식 코드Towards Real-World Blind Face Restoration with Generative Facial Prior
https://github.com/Tengfei-Wang/DualCameraSR
공식 코드https://github.com/ThibaultGROUEIX/AtlasNet
공식 코드AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
https://github.com/Thmen/EGVSR
공식 코드Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
https://github.com/Tillyhere/TADT
공식 코드Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution
https://github.com/Tombs98/MSSFNet
공식 코드Learning Accurate and Enriched Features for Stereo Image Super-Resolution
https://github.com/USArmyResearchLab/ARL_Conditional_Normalizing_Flows
공식 코드https://github.com/USTC-IMCL/HST-for-Compressed-SR
공식 코드HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
https://github.com/Usman1021/dual-attention
공식 코드DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
https://github.com/V-Sense/360SR
공식 코드Super-resolution of Omnidirectional Images Using Adversarial Learning
https://github.com/Vchitect/LaVie
공식 코드LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
https://github.com/VedikaSrivastava/GeoMapper
공식 코드https://github.com/W-JG/RAP-SR
공식 코드RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution
https://github.com/WCHN/CA_MTV-preproc
공식 코드https://github.com/Waller-Lab/SpeckleFlowSIM
공식 코드Dynamic Structured Illumination Microscopy with a Neural Space-time Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.