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Video Super-Resolution

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하위 태스크: 1
평가표: 16
보존된 평가 기록: 247

Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.

<span style="color:grey; opacity: 0.6">( Image credit: Detail-revealing Deep Video Super-Resolution )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jshermeyer/VDSR4Geo

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/jupiterman/Super-Resolution-Images

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/kadirnar/bsrgan-pip

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

https://github.com/kai422/iart

Enhancing Video Super-Resolution via Implicit Resampling-based Alignment

https://github.com/khyeyoon/Video-Super-Resolution

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/kingcheng2000/GAN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/kingcong/gpu_ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/koi953215/NaRCan

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

https://github.com/kornia/kornia

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/kwantommy/breast-cancer-diagnosis

XGBoost: A Scalable Tree Boosting System

https://github.com/l3th4l/SuperRes

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/laomao0/BIN

Video Enhancement with Task-Oriented Flow

https://github.com/laowng/GISR

Deep Back-Projection Networks For Super-Resolution

https://github.com/lee-aaron/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/leftthomas/espcn

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/GAN-training-tricks

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/Tips-and-tricks-to-train-GANs

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/liuzhen03/ADNet

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/lizhuoq/ESRGAN-pytorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/lucasmfaria/Machine-Learning-Capstone

XGBoost: A Scalable Tree Boosting System

https://github.com/luciennnnnnn/dualformer

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/markmaxt/VideoSR

Deep Back-Projection Networks For Super-Resolution

https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/med-seg/kidney

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/med-seg/kidney-mc

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mediatek-neuropilot/mai22-real-time-video-sr

Revisiting Temporal Modeling for Video Super-resolution

https://github.com/michael13162/DoodleGAN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/mtorabirad/PricePrediction

XGBoost: A Scalable Tree Boosting System

https://github.com/mukul1093/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/murrman95/INF573Project2018

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/mv-lab/swin2sr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/nabarunbaruaAIML/ATCC_Yolov5

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.