https://github.com/srpkdyy/VideoLDM
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/ssulun/pytorch-pixelshuffle1d
https://github.com/stability-ai/stablediffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/stekern/terraform-aws-paper2remarkable-lambda
https://github.com/steveazzolin/SinGan3D
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/subinium/Pytorch-GAN-Keynote-KR
https://github.com/sunilbelde/Imagedenoising-dncnn-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/sunilbelde/Imagedenoising-dncnn-ridnet-keras
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/suzana-ilic/DCGANs_pytorch
https://github.com/suzana-ilic/pytorch_DCGANs
https://github.com/sweetcocoa/IKC
https://github.com/swordgeek/SR
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/swz30/CycleISP
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/swz30/restormer
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/syahdeini/gan
https://github.com/szWingLee/spsr-master
Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/t-ae/singan-s4tf
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/t0nberryking/DCGAN256
https://github.com/takuseno/singan-nnabla
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tanjeffreyz/generative-adversarial-networks
https://github.com/teakkkz/imageSR
https://github.com/tegg89/SRCNN-Tensorflow
https://github.com/tengfei-wang/dcsr
https://github.com/tensorflow/models
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/tensorflow/models/tree/master/research/gan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tensorflow/models/tree/master/research/object_detection
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/tensorlayer/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tetrachrome/subpixel
https://github.com/tg-bomze/Face-Depixelizer
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/thatbrguy/Dehaze-GAN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/the-learning-and-vision-atelier-lava/pam
Parallax Attention for Unsupervised Stereo Correspondence Learning
https://github.com/theotziol/Depth-images-Denoising
https://github.com/thstkdgus35/EDSR-PyTorch
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/thudm/cogview4
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
https://github.com/thudm/visualglm-6b
CogView: Mastering Text-to-Image Generation via Transformers
https://github.com/tianbaochou/YOUKU-VSRE-2019-49th
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.