https://github.com/yzhao062/yzhao062
Diffusion Models: A Comprehensive Survey of Methods and Applications
Papers with Code 아카이브
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/zhanghang1989/PyTorch-Multi-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/zhangtianmingxp/rbpn_mindspore
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/zhangzhihengcn/SRGAN-Final-Version-Pytorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/zhoushen1/MEASNet
See More Details: Efficient Image Super-Resolution by Experts Mining
https://github.com/zhusiling/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/zhusiling/RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/zhusiling/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/zhy1001/Fast_Style_Transfer_using_CNN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/zijundeng/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ziyangwang007/mamba-unet
https://github.com/zlf1993/SrGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/zsdonghao/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/zsyOAOA/VDNet
https://github.com/zvict/papr
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
https://huggingface.co/isp-uv-es/superIX
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.