https://github.com/bennyqp/artificial-inspiration
SinGAN: Learning a Generative Model from a Single Natural Image
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/beresandras/gan-flavours-keras
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/boranhan/geospatial_foundation_models
Towards Geospatial Foundation Models via Continual Pretraining
https://github.com/bourcierj/rdfia-tme10-gan
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/LPSR-Recognition
https://github.com/brightyoun/Video-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/bruinxiong/RNAN
https://github.com/buddly27/stylish
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/bw2color/bw2color
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/camilocarvajalreyes/sfws-stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/catalyst-team/gan
https://github.com/chandlersupple/GAN-in-Tensorflow
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/chl218/DCNN-on-FPGA
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
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/christina-winkler/master-thesis-project
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/clarken92/vfm
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/clementchadebec/benchmark_VAE
Learning to Generate Images with Perceptual Similarity Metrics
https://github.com/cloneofsimo/consistency_models
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/Code2/tree/main/APDrawingGAN
https://github.com/code-implementation1/Code3/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.