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/jamesgolden1/mri_super_resolution
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/jamesloyys/PyTorch-Lightning-GAN
https://github.com/jangsoopark/IDN-TensorFlow
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
https://github.com/janhauffen/Photothermal_SR_Net
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging
https://github.com/jason-zl190/sisr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jattardo1/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/jayChung0302/DeepFilter
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/jaybhat02/summer_fourier_neural_operator
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/jaydeepthik/keras-GAN
https://github.com/jaysonph/Pytorch_GAN_MNIST
https://github.com/jcao216/DAIN_Modified
https://github.com/jiajunhua/aitorzip-PyTorch-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jiangzhongkai/ifly-algorithm_challenge
https://github.com/jiean001/models_m/tree/main/IRN
https://github.com/jihoojo03/UNet-CBAM_Keras
https://github.com/jingwei-bao/t-invblocks
Plug-and-Play Tri-Branch Invertible Block for Image Rescaling
https://github.com/jingyang2017/Face-and-Image-super-resolution
To learn image super-resolution, use a GAN to learn how to do image degradation first
https://github.com/jiny2001/deeply-recursive-cnn-tf
Deeply-Recursive Convolutional Network for Image Super-Resolution
https://github.com/jixiaozhong/RealSR
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/jlanday/Language-Detection
https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction
https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons
https://github.com/jlanday/X-ray-Object-Classification
https://github.com/jmalhot/SuperResolution-Computer-Vision-Using-GAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jmrf/dcscn-super-resolution
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
https://github.com/joanrod/figure-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/joanrod/ocr-vqgan
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/joeylitalien/celeba-gan-pytorch
https://github.com/joezengcbs/mini_xgboost_from_scratch
https://github.com/joh-fischer/PlantLDM
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/johnathanchiu/audio-upsampling
https://github.com/jojonki/arxiv-clip
https://github.com/josiahwsmith10/Efficient-3-D-Near-Field-MIMO-SAR-Imaging-for-Irregular-Scanning-Geometries
A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution
https://github.com/josiahwsmith10/dual-radar-gui
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution
https://github.com/josiahwsmith10/dual-radar-synchronizer
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.