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/b5071/pulse-rev
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/basilevh/image-outpainting
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/bdehaine/SinGan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/behnoudshafizadeh/ESPCN-SuperResolution-Tensorflow
https://github.com/bencottier/GANCS
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/bennyqp/artificial-inspiration
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/bestaar/predictiveFilterFlow
https://github.com/bochaozhao/Super_resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/brade31919/SRGAN-tensorflow
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/brenoskuk/recvis
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/brightyoun/Video-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/buddly27/stylish
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/cai631/mbmfn
Lightweight Image Super-Resolution with Information Multi-distillation Network
https://github.com/calebemonteiro/AIDL_Project
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/chaoxu0512/Pushbroom-satellite-image-SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/chilung/NCTU_Adv_DNN_HW4
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/chintan1995/Image-Denoising-using-Deep-Learning
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/chrismgeorge/Artistic_Additions_To_Style_Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/christiankellernc/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/chuanli11/MGANs
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/cis-cs-capstone-course/GAN_Image_Editor
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/code-implementation1/Code1/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/code-implementation1/Code3/tree/main/esr_ea
Efficient Residual Dense Block Search for Image Super-Resolution
https://github.com/code-implementation1/Code4/tree/main/IRN
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network
https://github.com/code-implementation1/Code6/tree/main/Pix2PixHD
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/code-implementation1/Code6/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/code-implementation1/Code6/tree/main/ras
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/code-implementation1/Code7/tree/main/RDN
https://github.com/coloquinte/torchsr
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/cryu854/FastStyle
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/cryu854/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/cviaai/PSF-GAN-ESTIMATION
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/cxzhou95/XLSR
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
https://github.com/daheyinyin/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/daitao/SAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.