https://github.com/mindspore-ai/models/tree/master/research/cv/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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/mindspore-ai/models/tree/master/research/cv/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/IPT
https://github.com/mindspore-ai/models/tree/master/research/cv/IRN
https://github.com/mindspore-ai/models/tree/master/research/cv/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/mindspore-ai/models/tree/master/research/cv/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/mindspore-ai/models/tree/master/research/cv/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/mindspore-ai/models/tree/master/research/cv/esr_ea
Efficient Residual Dense Block Search for Image Super-Resolution
https://github.com/mindspore-ai/models/tree/master/research/cv/wdsr
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/mindspore-lab/mindone
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mingcv/ytmt-strategy
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/miriskalt/AnomalyDetectionAutoencoder
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/mirthai/med3dvlm
Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/mizimo/GAN_EMNIST
https://github.com/mlaskowski17/Pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/mmbajo/PROBA-V
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/modeloriented/fairpan
https://github.com/mohit1997/ZSSR
https://github.com/monishramadoss/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/mshaikh2/GANs_Comparison
https://github.com/msmsajjadi/EnhanceNet-Code
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/mtorabirad/PricePrediction
https://github.com/mukul1093/Image-Super-Resolution
https://github.com/murrman95/INF573Project2018
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/myoons/GAN-pytorch
https://github.com/nSamsow/CSNLN-new
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
https://github.com/nabarunbaruaAIML/ATCC_Yolov5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nagadomi/waifu2x
https://github.com/nannau/DoWnGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/nboulle/pde-learning
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/nec-research/cape-ml4sci
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/nekitmm/starnet
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/neural-operator/pino
Physics-Informed Neural Operator for Learning Partial Differential Equations
https://github.com/neuraloperator/physics_informed
Physics-Informed Neural Operator for Learning Partial Differential Equations
https://github.com/nhatsmrt/superres
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.