Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/nickhnelsen/error-bounds-for-vvRF
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/nickhnelsen/fourier-neural-mappings
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/nickhnelsen/random-features-banach
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/nihui/realsr-ncnn-vulkan
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/nikhilsu/SuperPixel
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
https://github.com/nikunj-taneja/accendo
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ninaqy/PUGeo
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
https://github.com/ninatu/mood_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/nitaifingerhut/SinGAN-for-Object-Removal
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/nmhkahn/CARN-pytorch
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
https://github.com/nonslowrunner/GAN-Upscale-Image
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/noobpython/Notes
https://github.com/noufali/VideoML
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/noyomer/few-shots-generation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/npielawski/pytorch_tiramisu
https://github.com/nsom/Basic-Models
https://github.com/nsom/VGG16
https://github.com/nsubbaian/FrequentistML
https://github.com/nunof98/AiVision_Anomaly_Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/ocinemod87/AML_project
https://github.com/oellop/GAN_MNIST
https://github.com/ofsoundof/Heterogeneity_Hypothesis
Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ofsoundof/group_sparsity
Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/oktaykarakus/CauchySAR-v1.0
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
https://github.com/olaviinha/NeuralImageSuperResolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/omermosa/Single-Image-Colorization-and-Super-Resolution
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/omglu93/energy_consumption_eda_prediction
https://github.com/omkarghugarkar007/Neural_Super_Sampling
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/oneTaken/pytorch_fast_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/oopsoopskeke/LaonSillv2
https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/open-mmlab/mmgeneration
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/open-mmlab/mmsr
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/openai/consistency_models_cifar10
https://github.com/openai/consistencydecoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.