Papers with Code 아카이브
3D Object Super-Resolution
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3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hejingwenhejingwen/AdaFM
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
https://github.com/hekstra-lab/phase-retrieval
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/herbiebradley/notes-template
https://github.com/heshitian/plug
Real-World Light Field Image Super-Resolution via Degradation Modulation
https://github.com/hnt4499/DeFlow
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
https://github.com/hongpeng-guo/deep-image-prior
https://github.com/htzheng/ECCV2018_CrossNet_RefSR
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
https://github.com/huawei-noah/Efficient-Computing
https://github.com/huyz1117/GAN-TensorFlow
https://github.com/huzexi/huzexi
Efficient Light Field Reconstruction via Spatio-Angular Dense Network
https://github.com/hy-zpg/DAGAN
https://github.com/icey-zhang/ClassSR_paddle
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
https://github.com/icey-zhang/paddle_SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/idealo/image-super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/idearibosome/tf-perceptual-eusr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/idearibosome/tf-sr-attack
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
https://github.com/illume-unified-mllm/ILLUME_plus
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
https://github.com/ilshatus/nn_style_transfer_cv_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ilyak93/SinGan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ilyak93/SinGanF2
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ilyakava/gan
https://github.com/image-science-lab/OneNet
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
https://github.com/impredicative/irc-url-title-bot
https://github.com/imransalam/JJ-style-transfer-TensorFlow2.0
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ipc-lab/deepjscc-diffusion
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/iridescentttt/SynQT
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
https://github.com/isaaccorley/torchrs
https://github.com/itsuki8914/ESRGAN-TensorFlow
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/itsuki8914/SRGAN-TensorFlow
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/itsuki8914/WGAN-GP-ResBlock-TensorFlow
https://github.com/itsuki8914/srresnet4x
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/ivrl/pogain
W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping
https://github.com/jabir-zheng/TCD
https://github.com/jacquelinelala/GFN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/jaingaurav3/GAN-Hacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.