https://github.com/alterzero/RBPN-PyTorch
The 2018 PIRM Challenge on Perceptual Image Super-resolution
Papers with Code 아카이브
Image Super-Resolution
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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/alterzero/STARnet
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/alvarobasi/eyeSRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/amanshenoy/image-super-resolution
https://github.com/amazingren/ntire2025_esr
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
https://github.com/amazingyyc/Brouhaha
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/amiq-consulting/image-upscaling-CNN
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/amogh7joshi/media-vision
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/amzamzamzamz/nagadomi-waifu2x
https://github.com/anastassia-b/neural-algorithm-artistic-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/andreamazzitelli/ProjectNN
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/andreas128/NTIRE21_Learning_SR_Space
https://github.com/andreasfloros/IRN
https://github.com/andrerclaudio/channel_estimation
https://github.com/andrewhproppe/phaseretrievalnns
https://github.com/anhkhoa039/Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/anjalipemmaraju/styletransfernetwork
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/anktplwl91/Image-Superresolution
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/anujdutt9/ESPCN
https://github.com/anujtyagi2802/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/anushkayadav/Denoising_cifar10
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/ariel415el/Simple-SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/arjunarao619/SRCNN_Pytorch
https://github.com/arm-software/ml-restructurable-activation-networks
Collapsible Linear Blocks for Super-Efficient Super Resolution
https://github.com/arsenalcheng/mma
https://github.com/aryan-mann/style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ashkanpakzad/atn
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/asraman9792/Image-Dehazing-GANS
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/assafshocher/ZSSR
https://github.com/atheesh1998/Image-Super-Resolution
https://github.com/atriumlts/subpixel
https://github.com/ayanglab/swinganmr
https://github.com/ayanglab/swinmr
https://github.com/aynetdia/mtp_curriculum
https://github.com/ayushmankumar7/SISR-Tensorflow-2.x
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.