https://github.com/ayanglab/thick2thin
공식 코드Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
Papers with Code 아카이브
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/bahjat-kawar/ddrm
공식 코드https://github.com/bahjat-kawar/snips_torch
공식 코드https://github.com/baidu/AdvBox
공식 코드A Tour of Convolutional Networks Guided by Linear Interpreters
https://github.com/bashar-tahir/plain
공식 코드PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication
https://github.com/basiralab/AGSR-Net
공식 코드https://github.com/basiralab/GSR-Net
공식 코드https://github.com/basiralab/L2S-KDnet
공식 코드Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning
https://github.com/basiralab/sg-net
공식 코드StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis
https://github.com/batmanlab/ha-gan
공식 코드Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
https://github.com/bcv-uniandes/rsr
공식 코드Generalized Real-World Super-Resolution through Adversarial Robustness
https://github.com/bcv-uniandes/superformer
공식 코드SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution
https://github.com/beanham/2023-urban-disaggregation
공식 코드SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation
https://github.com/berkdogan2/GWAInet
공식 코드Exemplar Guided Face Image Super-Resolution without Facial Landmarks
https://github.com/berumotto-vermouth/S2R
공식 코드S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
https://github.com/bia006/darts
공식 코드DARTS: Double Attention Reference-based Transformer for Super-resolution
https://github.com/bia006/hitsr
공식 코드HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution
https://github.com/bichunyang419/deedsr
공식 코드DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
https://github.com/billhhh/FQSR
공식 코드https://github.com/biomedai-ucsc/inversesr
공식 코드InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
https://github.com/bipin-saha/bnvd
공식 코드https://github.com/bitszwang/dpt
공식 코드Detail-Preserving Transformer for Light Field Image Super-Resolution
https://github.com/bjmch/MRVSR
공식 코드https://github.com/bkainz/fetalReconstruction
공식 코드PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
https://github.com/bkoyuncu/vamoh
공식 코드Variational Mixture of HyperGenerators for Learning Distributions Over Functions
https://github.com/boa2004plaust/vimd
공식 코드Vision Mamba Distillation for Low-resolution Fine-grained Image Classification
https://github.com/boelukas/mariner
공식 코드MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby References
https://github.com/booooooooooo/cri
공식 코드High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets
https://github.com/brian-moser/diwa
공식 코드Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
https://github.com/brudfors/spm_superres
공식 코드https://github.com/bu-cisl/LCNF
공식 코드https://github.com/budai4medtech/midl2023
공식 코드https://github.com/bugsuse/weather4cast-2022-stage2
공식 코드Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormers
https://github.com/bychelsea/cmos
공식 코드https://github.com/byronliang8/ida-rd
공식 코드Deep Generative Model based Rate-Distortion for Image Downscaling Assessment
https://github.com/calum-green/xct-xdrct_paper_code
공식 코드Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.