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/etjoa003/medical_imaging
https://github.com/etttttte/mayfest2018
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/everything2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/explainingai-code/StableDiffusion-PyTorch
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/fangchangma/sparse-to-dense.pytorch
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/fannymonori/TF-LapSRN
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
https://github.com/fashn-AI/tryondiffusion
Simple diffusion: End-to-end diffusion for high resolution images
https://github.com/fenghansen/ESRGAN-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fengwang/subpixel_conv2d
https://github.com/fengye-lu/DRN-master
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
https://github.com/fengye-lu/PyTorch-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/fengye-lu/RCAN-pytorch
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/ferseiti/reproducibility
https://github.com/fhshen2022/prunerepaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/final-0/Real-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/final-0/Real-ESRGAN-bicubic
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fourseaforfriend/waifu2x
https://github.com/fperazzi/proSR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/fpramunno/InDI-implementation
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
https://github.com/francis-kang/handson-unsupervised-learning
https://github.com/franckbrl/nmt-pseudo-source-discriminator
https://github.com/francois-rozet/mmps-benchmark
Denoising Diffusion Models for Plug-and-Play Image Restoration
https://github.com/friedmanroy/hi-generation
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/fsalmasri/MSF-STI-SR
Multimodal Sensor Fusion In Single Thermal image Super-Resolution
https://github.com/fzimmermann89/sr4rs
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/g4vrel/simple_ml/tree/main/GAN
https://github.com/galprz/ZSSR-pytorch
https://github.com/gan3sh500/custom-pooling
Advanced Super-Resolution using Lossless Pooling Convolutional Networks
https://github.com/gaoalexander/ML-Project-Generative-Adversarial-Network
https://github.com/gargarchit/60DaysOfUdacity
https://github.com/geekyutao/inpaint-anything
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/gegewen/ufno
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/genforce/fairgen
Improving the Fairness of Deep Generative Models without Retraining
https://github.com/geonm/EnhanceNet-Tensorflow
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/georgeyiasemis/RIM-PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.