https://github.com/yjn870/FSRCNN-pytorch
Accelerating the Super-Resolution Convolutional Neural Network
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/yjn870/REDNet-pytorch
https://github.com/yjn870/WDSR-pytorch
Wide Activation for Efficient and Accurate Image Super-Resolution
https://github.com/yjn870/rdn-pytorch
https://github.com/yjn870/srcnn-pytorch
https://github.com/yokotatsuya/MMES
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image Prior
https://github.com/yoon28/pseudo-sr
https://github.com/yoon28/unpaired_face_sr
To learn image super-resolution, use a GAN to learn how to do image degradation first
https://github.com/yortyj/Exploiting-GANs-for-Phun-and-Profit
https://github.com/youdit/low-resolution-to-high-resolution-image
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ys-koshelev/lgd
https://github.com/yslenjoy/machine_learning_SuperResolution
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/ytsheng/sketch2cat-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/yuanjunchai/IKC
https://github.com/yuanxiaoyu0402/deep
https://github.com/yuepingwang/sngan-projection
https://github.com/yueqianlongma/GAN
https://github.com/yuheng-li/universalfakedetect
CNN-generated images are surprisingly easy to spot... for now
https://github.com/yukitsuji/3D_CNN_tensorflow
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
https://github.com/yyunon/reproducibility-project-group-71
https://github.com/yzhao062/yzhao062
Diffusion Models: A Comprehensive Survey of Methods and Applications
https://github.com/zcemycl/Matlab-GAN
https://github.com/zeeshannisar/Generative-Models-Papers-with-Implementation-in-Keras/tree/master/GAN-Generative%20Adversarial%20Network
https://github.com/zekedran/deep_image_prior
https://github.com/zgctroy/layoutdiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/zh320/realtime-semantic-segmentation-pytorch
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/zhaohui-yang/Binary-Neural-Networks
https://github.com/zhaozixiang1228/mmif-cddfuse
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
https://github.com/zhiyuns/REHRSeg
https://github.com/zhiyuns/UniCOAL
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
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/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.