https://github.com/opteroncx/SESR
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
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/osofr/xgboost
https://github.com/ozcelikfu/PanColorGAN
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
https://github.com/p-hss/consistency-climate-downscaling
https://github.com/pankajb64/bayescnn
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/pdebench/pdebench
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pharikal/Breast-Cancer-Detection-Using-Deep-Learning
https://github.com/pierobeat/Hoax-News-Classification
https://github.com/pilot7747/sldl
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/pminhtam/DeepBurstDenoising
https://github.com/pminhtam/MIRnet_SIDD
Learning Enriched Features for Real Image Restoration and Enhancement
https://github.com/pminhtam/MWCNN
https://github.com/pmsharkKOR/Deep-Learning-Examples---Awesome-Resourse1
https://github.com/pnavarre/pirm-sr-2018
Multigrid Backprojection Super-Resolution and Deep Filter Visualization
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/poikilos/pyrotocanvas
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/poyushen/classifaction
https://github.com/pputzky/invertible_rim
https://github.com/pranavbudhwant/ACVAEGAN
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/pranshupant/SR-DNS_Net
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
https://github.com/pratik-kubal/Deep-Laplacian-Pyramid-Networks
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
https://github.com/primecai/pix2nerf
Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation
https://github.com/programmingmlpapers/pytorch-GAN
https://github.com/proteus1991/GridDehazeNet
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/pskrunner14/face-DCGAN
https://github.com/psychopa4/PFNL
https://github.com/ptrblck/prog_gans_pytorch_inference
https://github.com/puffnjackie/pytorch-super-resolution-implementations
https://github.com/pwc-1/Paper-10/tree/main/swin2sr
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d
https://github.com/qibao77/LFFN
Lightweight Feature Fusion Network for Single Image Super-Resolution
https://github.com/qinpengcui/dossr
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
https://github.com/qinpengzhi/myDCGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.