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/shangqigao/RONet
Rank-One Network: An Effective Framework for Image Restoration
https://github.com/shangqigao/bayesr
Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
https://github.com/shaonianruntu/MWCNN
https://github.com/shawnrosofsky/pino_applications
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/shesay-noway/Super-Resolution-RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/shibuiwilliam/DeepLearningDenoise
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/shimo8810/NTIRE2017
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/shirsenduhalder/SRResGAN-Improved-Perceptual
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/shivsundram/superresolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/shnhrtkyk/satellite-super-resolution
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/showlab/Tune-A-Video
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/showlab/loveu-tgve-2023
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/shreeyashyende/better_img_res_with_SRCNN
https://github.com/siavashbigdeli/DMSP
https://github.com/simon0987/ACGAN-implemented-with-pytorch
https://github.com/simonsLiang/PReNet_paddle
Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/singhsidhukuldeep/Generative-Adversarial-Network-GAN
https://github.com/singnet/super-resolution-service
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/skchen1993/SwinIR
https://github.com/sldyns/DnCNN_paddle
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sldyns/MIRNet_paddle
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/sliao-mi-luku/GAN-food-image-generator
https://github.com/slimgroup/dfno
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/slimgroup/fno4co2
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/smc-x/ms-irn
https://github.com/sobieskibj/rcsb
https://github.com/soham239/MLND_Capstone_Project
https://github.com/sooooner/MNIST-GAN-with-tensorflow2
https://github.com/sooyekim/3DSRnet
3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks
https://github.com/soumik-kanad/diffssl
https://github.com/spengliang/smoothvideo
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/sreerajr000/consistency-models
https://github.com/sreyas-mohan/DeepFreq
A Learning-Based Framework for Line-Spectra Super-resolution
https://github.com/srikanthmandru/Image-Super-Resolution-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/srikanthmandru/Super-Resolution-of-Images-Videos-using-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.