https://github.com/havrylovv/isegprobe
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
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/hejingwenhejingwen/AdaFM
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
https://github.com/hekstra-lab/phase-retrieval
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/heshitian/plug
Real-World Light Field Image Super-Resolution via Degradation Modulation
https://github.com/huawei-noah/Efficient-Computing
https://github.com/huzexi/huzexi
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution
https://github.com/icandle/plainusr
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
https://github.com/icey-zhang/paddle_SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/idealo/image-super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/idearibosome/tf-perceptual-eusr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/idearibosome/tf-sr-attack
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
https://github.com/ilshatus/nn_style_transfer_cv_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ilyak93/SinGan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ilyak93/SinGanF2
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/imatge-upc/3D-GAN-superresolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/impredicative/irc-url-title-bot
https://github.com/imransalam/JJ-style-transfer-TensorFlow2.0
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ipc-lab/deepjscc-diffusion
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/iridescentttt/SynQT
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
https://github.com/isaaccorley/torchrs
https://github.com/itsuki8914/ESRGAN-TensorFlow
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/itsuki8914/SRGAN-TensorFlow
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/itsuki8914/srresnet4x
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jacquelinelala/GFN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jaingaurav3/GAN-Hacks
https://github.com/jaivanti/Super-Resolution-using-ConvNet
https://github.com/jamesgolden1/mri_super_resolution
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/jangsoopark/IDN-TensorFlow
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
https://github.com/jason-zl190/sisr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jattardo1/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/jayChung0302/DeepFilter
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/jiajunhua/aitorzip-PyTorch-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jiean001/models_m/tree/main/IRN
https://github.com/jimmycv07/DiffIR2VR-Zero
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
https://github.com/jingwei-bao/t-invblocks
Plug-and-Play Tri-Branch Invertible Block for Image Rescaling
https://github.com/jingyang2017/Face-and-Image-super-resolution
To learn image super-resolution, use a GAN to learn how to do image degradation first
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.