Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/IBerdinskikh/NCSR
Noise Conditional Flow Model for Learning the Super-Resolution Space
https://github.com/IIplutocrat45II/ARTEST
https://github.com/IMAC-projects/Deblurring-PyTorch
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
https://github.com/IMAC-projects/SRN-Deblurring-PyTorch
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
https://github.com/IRVLab/Deep-SESR
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
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
https://github.com/JVision/Handheld-Multi-Frame-Super-Resolution
https://github.com/JWSoh/HDRI-SR
Joint High Dynamic Range Imaging and Super-Resolution from a Single Image
https://github.com/JWSoh/MainSR
https://github.com/JackonLiu/SinGAN-Easy
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/JaguAroo/DASR
Unsupervised Degradation Representation Learning for Blind Super-Resolution
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/JaryV/CycleGAN_OldYoung
https://github.com/JasonPlawinski/SuperResolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Javihaus/Auto-ML-app-with-dash
https://github.com/JihyunLee9805/GMFN
https://github.com/JimmyDoan1309/GAN-DCGAN
https://github.com/JoegameZhou/ms-irn
https://github.com/JohnRomanelis/SPVD_Lightning
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
https://github.com/JojiJoseph/DCGAN
https://github.com/JojiJoseph/GAN
https://github.com/JonasRSV/GAN
https://github.com/JonathanLehner/TecoGan-downloader
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/Joool/ReproducabilityCNNEasyToSpot
CNN-generated images are surprisingly easy to spot... for now
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.