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/meitalB/NN
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/mengxiangming/qcs-sgm
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
https://github.com/michael13162/DoodleGAN
https://github.com/micmic123/qmapcompression
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
https://github.com/mike9251/DAQ
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
https://github.com/mileyan/pseudo-LiDAR_e2e
https://github.com/milmor/perceptual-losses-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d
https://github.com/mindspore-ai/contrib/tree/master/papers/CSD
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/mindspore-ai/models/blob/master/research/cv/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/mindspore-ai/models/blob/master/research/cv/wdsr
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/mindspore-ai/models/tree/master/official/cv/RDN
https://github.com/mindspore-ai/models/tree/master/official/cv/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn
https://github.com/mindspore-ai/models/tree/master/research/cv/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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/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/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/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/mukul1093/Image-Super-Resolution
https://github.com/murrman95/INF573Project2018
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.