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/HarukiYqM/pytorch-ZSSR
https://github.com/Hayashi-Yudai/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/HighVoltageRocknRoll/sr
https://github.com/HilaManor/Generative-deep-features
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/HritwikEkade/Image-Resolution
https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/IBM/MAX-Image-Resolution-Enhancer
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/IRVLab/srdrm
Underwater Image Super-Resolution using Deep Residual Multipliers
https://github.com/Idelcads/IMKI_Technical_test
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Idelcads/Super_Resolution_overview
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Iheb96/SinGAN_InPainting
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/IlyaKodua/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/Intelligent-Imaging-Center/Spectral-Reconstruction
An efficient CNN for spectral reconstruction from RGB images
https://github.com/JVision/Handheld-Multi-Frame-Super-Resolution
https://github.com/JWSoh/MainSR
https://github.com/JackonLiu/SinGAN-Easy
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Jakaria08/EESRGAN
https://github.com/Jakaria08/F-EESRGAN
https://github.com/Jan-jia/Paddle-RCAN
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/JannaShen/DBDN
https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
https://github.com/JasonPlawinski/SuperResolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/JihyunLee9805/GMFN
https://github.com/JoegameZhou/ms-irn
https://github.com/JonathanLehner/TecoGan-downloader
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/Joostdambrink/GeniusAI
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/Josien94/MLiP
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Karan-kapadia/Style-Transfer-karan
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/KeremTurgutlu/papers
https://github.com/Kumara-Kaushik/proba_v_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/LCM1999/VolumeRescaling
https://github.com/LEEPEIQIN/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LONG-9621/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/LWChen20/RCAN
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/LYL1015/ResDiff
ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.