Alternatives and similar repositories for HandyLatex

Users that are interested in HandyLatex are comparing it to the libraries listed below

• MKFMIKU / d2sm
  Deep Semantic Statistics Matching (D2SM) Denoising Network (ECCV22)
  ☆24Updated 3 years ago
• fangwei123456 / PixelUnshuffle-pytorch
  PixelUnshuffle, inverse operation of PixelShuffle
  ☆21Updated 4 years ago
• HDCVLab / UHD4K_UHD8K
  ☆38Updated last year
• affromero / NTIRE22_Inpainting
  NTIRE 2022 - Image Inpainting Challenge
  ☆48Updated 3 years ago
• xrenaa / Deblur-by-Fitting
  Implementation of “Video Deblurring by Fitting to Test Data“: https://arxiv.org/abs/2012.05228
  ☆26Updated 4 years ago
• njulj / AdaDM
  AdaDM: Enabling Normalization for Image Super-Resolution
  ☆60Updated 3 years ago
• XPixelGroup / CloserLookBlindSR
  Paper accepted by CVPR Workshop, NTIRE 2022
  ☆29Updated last year
• peiwang062 / swag
  ☆73Updated 4 years ago
• entavelis / ScaleParty
  ☆43Updated 2 years ago
• WenxueCui / Transformer_Low_Level_Vision
  Transformer for low-level vision applications, such as image restoration (denosing, super resolution, deblur)
  ☆54Updated 3 years ago
• hqqxyy / VFIPS
  The PyTorch implementation for A Perceptual Quality Metric for Video Frame Interpolation
  ☆43Updated last year
• z-bingo / Contextual-Loss-PyTorch
  An implement of contextual loss using PyTorch.
  ☆48Updated 6 years ago
• yhjo09 / AdaTarget
  ☆32Updated 3 months ago
• liuzhuang13 / Transferable_RA
  ☆37Updated 2 years ago
• arthurhero / deep_fill_2_pytorch
  Pytorch implementation of deep fill v2 (original by Jiayu et al.)
  ☆10Updated 6 years ago
• MingSun-Tse / SRP
  [ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning"
  ☆29Updated 2 years ago
• TencentARC / FAIG
  NeurIPS 2021, Spotlight, Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution
  ☆119Updated 2 years ago
• BCV-Uniandes / RSR
  This is the official implementation of the paper: Generalized Real-World Super-Resolution through Adversarial Robustness.
  ☆16Updated 3 years ago
• littleYaang / HQ-50K
  HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
  ☆84Updated last year
• ychfan / nsr
  Neural Sparse Representation for Image Restoration
  ☆31Updated 5 years ago
• littlepure2333 / SamplingAug
  SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution (BMVC2021)
  ☆39Updated 3 years ago
• yuke93 / Path-Restore
  Path-Restore: Learning Network Path Selection for Image Restoration (IEEE TPAMI, 2021)
  ☆42Updated 3 years ago
• ychfan / pt_ir
  Image Restoration Toolkit in PyTorch
  ☆37Updated 5 years ago
• lpj0 / CBSR
  Learning Cascaded Convolutional Networks for Blind Single Image Super-Resolution
  ☆21Updated 5 years ago
• VITA-Group / SSHarmonization
  [ICCV'2021] "SSH: A Self-Supervised Framework for Image Harmonization", Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalya…
  ☆102Updated 2 years ago
• YunanZhu / RecycleD
  Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein…
  ☆25Updated 3 years ago
• visinf / deblur-devil
  Deep Video Deblurring: The Devil is in the Details (ICCV Workshop 2019)
  ☆18Updated 5 years ago
• xindongzhang / SimpleIR
  A Simple framework for image restoration, it includes ECBSR, ELAN and other SOTAs.
  ☆49Updated 2 years ago
• yzxing87 / pytorch-deep-video-prior
  [NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior
  ☆128Updated 2 years ago
• parkseobin / MLSR
  Source code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"
  ☆67Updated 2 years ago