https://github.com/qiyuantian/DeepDTI
SRDTI: Deep learning-based super-resolution for diffusion tensor MRI
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/quereste/implicit-deepfake
High-Resolution Image Synthesis with Latent Diffusion Models
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/rajprakrit/DCGAN
https://github.com/rdutta1999/Patched-Face-Regeneration-GAN
https://github.com/rdutta1999/Patched-Face-Regeneration-v2
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/remibasaru/GANs-Suite
https://github.com/renyang-home/aim22_compresssr
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
https://github.com/rharish101/DeFMO
https://github.com/rick-chang/OneNet
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
https://github.com/ricsonc/more-pixels-less-noise
https://github.com/rishikksh20/NU-Wave-pytorch
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
https://github.com/rishikksh20/NU-Wave2-pytorch
NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates
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/roberttwomey/machine-imagination-workshop
https://github.com/robo-warrior/SR
A Fully Progressive Approach to Single-Image Super-Resolution
https://github.com/rohitkuk/AnimeGAN
https://github.com/rohitkuk/Cartoonify
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/royson/tpsr
https://github.com/rozumden/tbd
https://github.com/rozumden/tbd3d
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/rsin46/deep-image-prior-keras
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.