https://github.com/woutercools1998/SRResNet
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial 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/wustl-cig/FLAIR
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
https://github.com/wwhio/megmodels
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/wwlCape/HAN
Single Image Super-Resolution via a Holistic Attention Network
https://github.com/xahidbuffon/Deep_SESR
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
https://github.com/xanderex-sid/FSRCNN
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/xg416/DATUM
Recurrent Video Restoration Transformer with Guided Deformable Attention
https://github.com/xgd/waifu2xx
https://github.com/xiadanqing/Binary
https://github.com/xiangyu-liu/DBSR
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/xiaomi-automl/FALSR
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
https://github.com/xinntao/BasicSR
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/xiuyu0000/papers_with_examples/tree/main/CAR
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
https://github.com/xiuyu0000/papers_with_examples/tree/main/srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/xoraus/Super-Resolution-CNN-for-Image-Restoration
https://github.com/xpixelgroup/ranksrgan
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
https://github.com/xpixelgroup/rdsr
Discovering Distinctive "Semantics" in Super-Resolution Networks
https://github.com/xuduo35/MakeLongVideo
Make-A-Video: Text-to-Video Generation without Text-Video Data
https://github.com/xuxy09/texformer
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
https://github.com/xxayt/mindspore-GAN
https://github.com/xxh96/ADCSR
https://github.com/xypeng9903/k-diffusion-inverse-problems
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/yakhyo/Fast-Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/yangling0818/contextdiff
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/yangyucheng000/CAR
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/IPT-2
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/yangyucheng000/IPT-3
https://github.com/yangyucheng000/Paper-2/tree/main/DWAL-ms-main
DWA: Differential Wavelet Amplifier for Image Super-Resolution
https://github.com/yangyucheng000/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/yangyucheng000/University/tree/main/model-3/swin2sr
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/yangyucheng000/srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/yhjo09/VSR-DUF
https://github.com/yifita/3PU
https://github.com/yilinliu97/fasterdip-devil-in-upsampling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.