https://github.com/tianxingxia-cn/LIIF-Paddle
Learning Continuous Image Representation with Local Implicit Image Function
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/tiasmondal/SRGAN-keras-google-colab
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tinnunculus/Denoising_SR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/titu1994/Image-Super-Resolution
https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tjjj686/dl_project_srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tohinz/ConSinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tom-doerr/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tom-doerr/TecoGAN-Docker
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tomtirer/BP-term
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse Problems
https://github.com/tomtrac/SRGAN_power_data_generation
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tony23545/SinGAN_
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/tqchen/xgboost
https://github.com/tryambak2019/SGAN
https://github.com/ttsalpha/generative-adversarial-networks
https://github.com/tudelft-iv/occluded_vehicle_acoustic_detection
Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners
https://github.com/tum-vision/learn_prox_ops
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/turboLIU/SRFBN-tensorflow
https://github.com/tuvovan/MIRNet---Keras
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/tuvovan/RFDNet-ImageSuperResolution
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/tuvovan/ZSSR-Keras
https://github.com/twhui/SRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/twice154/ofa-for-super-resolution
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
https://github.com/twtygqyy/pytorch-SRResNet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/tyshiwo/DRRN_CVPR17
https://github.com/u7javed/Generative-Adversarial-Network
https://github.com/u7javed/Image-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/u7javed/Image-Super-Resolution-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ubisoft/ubisoft-laforge-animation-dataset
https://github.com/ubisoftinc/Ubisoft-LaForge-Animation-Dataset
https://github.com/ubombar/super-resolution-srrnet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ucbdrive/3d-vehicle-tracking
https://github.com/umutsuluhan/HSTRNet-Interp
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
https://github.com/unicredit/ganzo
https://github.com/vaibhav369/gans
https://github.com/vainf/diff-pruning
High-Resolution Image Synthesis with Latent Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.