https://github.com/wwlCape/HAN
Single Image Super-Resolution via a Holistic Attention Network
Papers with Code 아카이브
Image Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 69
- 보존된 평가 기록
- 748
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/xanderex-sid/FSRCNN
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/xgd/waifu2xx
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/xieenze/TextSR
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/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/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/yjn870/FSRCNN-pytorch
Accelerating the Super-Resolution Convolutional Neural Network
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/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/youdit/low-resolution-to-high-resolution-image
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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/yukitsuji/3D_CNN_tensorflow
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.