https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Papers with Code 아카이브
Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/poikilos/pyrotocanvas
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/pranshupant/SR-DNS_Net
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
https://github.com/pratik-kubal/Deep-Laplacian-Pyramid-Networks
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
https://github.com/proteus1991/GridDehazeNet
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/puffnjackie/pytorch-super-resolution-implementations
https://github.com/pwc-1/Paper-10/tree/main/swin2sr
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d
https://github.com/qibao77/LFFN
Lightweight Feature Fusion Network for Single Image Super-Resolution
https://github.com/qinpengcui/dossr
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
https://github.com/quic/aimet-model-zoo
Collapsible Linear Blocks for Super-Efficient Super Resolution
https://github.com/quin-med-harvard-edu/kidney
https://github.com/r06922019/butt_lion_paper_notes
https://github.com/raahatg21/Creating-Artistic-Images
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rafcy/HarpyTM
https://github.com/rajatthepagal/DBPN-Keras
https://github.com/rami0205/ramit
N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution
https://github.com/rdutta1999/Patched-Face-Regeneration-v2
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/renyang-home/aim22_compresssr
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
https://github.com/ricsonc/more-pixels-less-noise
https://github.com/riven314/PerceptualLoss-FastAI
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/riyakothari/NeuralStyleTransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rkem1542/EDSR-pytorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/robo-warrior/SR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/roimehrez/PIRM2018
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/roimehrez/contextualLoss
Maintaining Natural Image Statistics with the Contextual Loss
https://github.com/rrmina/fast-neural-style-pytorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rrrepsac/tb_vc
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rshwndsz/denoiser
https://github.com/rupakurinchivendhan/wisosuper
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/ryanchankh/style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ryanhalabi/starcraft_super_resolution
https://github.com/ryanxingql/image-quality-assessment-toolbox
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/ryyAudrey/ConMixSinGAN-master
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/saeed-anwar/DRLN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.