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3D Object Super-Resolution
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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/amazingren/ntire2024_esr
공식 코드The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
https://github.com/amazingren/ntire2025_esr
공식 코드The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
https://github.com/amhamster/c2pd
공식 코드C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolution
https://github.com/amirdahari1/superres
공식 코드Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
https://github.com/anastasiia-kornilova/MMDF
공식 코드https://github.com/andreas128/SRFlow
공식 코드SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/andreasfloros/trustworthy-super-resolution
공식 코드Trustworthy Image Super-Resolution via Generative Pseudoinverse
https://github.com/aneesh3108/AeroRIT
공식 코드https://github.com/angelomenezes/Pytorch_Face_SR
공식 코드https://github.com/angelvillar96/super-resolution-noisy-images
공식 코드Deep learning architectural designs for super-resolution of noisy images
https://github.com/annegnx/PnP-Flow
공식 코드PnP-Flow: Plug-and-Play Image Restoration with Flow Matching
https://github.com/anonyr7/Sinogram-Inpainting
공식 코드2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
https://github.com/anshgs/earthgen
공식 코드https://github.com/anthonymlortiz/epitomes_lsr
공식 코드Mining self-similarity: Label super-resolution with epitomic representations
https://github.com/anthonyzhou-1/mae-pdes
공식 코드https://github.com/antoinedelplace/MRI-Generation
공식 코드Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks
https://github.com/antoinedelplace/MRI-Segmentation
공식 코드Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks
https://github.com/aounon/center-smoothing
공식 코드Center Smoothing: Certified Robustness for Networks with Structured Outputs
https://github.com/arashfree/TLW-in-Super-Resolution
공식 코드https://github.com/arcananana/cdcn
공식 코드Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
https://github.com/arcananana/dssr
공식 코드Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
https://github.com/arctichare105/s3diff
공식 코드Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
https://github.com/arsenal9971/poissonnet
공식 코드Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds
https://github.com/arturia-pendragon-iris/hipas_av_segmentation
공식 코드https://github.com/aselsan-research-imaging-team/bicubic-plusplus
공식 코드Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network
https://github.com/ashj9/AFN
공식 코드AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
https://github.com/asindel/VSRF
공식 코드Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
https://github.com/augusthoeg/mtvnet
공식 코드https://github.com/aulrichsen/sronn
공식 코드Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution
https://github.com/aupendu/sr-uncertainty
공식 코드https://github.com/aurangau/ICIP2024
공식 코드https://github.com/avc2-uestc/craft-sr
공식 코드Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
https://github.com/avc2-uestc/frequency-inspired-optimization-for-efficientsr
공식 코드Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution
https://github.com/avinashpaliwal/Deep-SloMo
공식 코드Deep Slow Motion Video Reconstruction with Hybrid Imaging System
https://github.com/avisekiit/TCSVT-LightWeight-CNNs
공식 코드Lightweight Modules for Efficient Deep Learning based Image Restoration
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