wangyunhong's repositories
low-light-image-enhancement-resources
This is a resouce list for low light image enhancement
awesome-computer-vision
A curated list of awesome computer vision resources
Awesome-Deblurring
A curated list of resources for Image and Video Deblurring
awesome-Face_Recognition
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;
Background-Matting
Background Matting: The World is Your Green Screen
CurvatureFilter
Curvature Filters are efficient solvers for Variational Models
cutblur
Rethinking Data Augmentation for Image Super-resolution (CVPR 2020)
CVPR2020-Paper-Code-Interpretation
cvpr2020/cvpr2019／cvpr2018/cvpr2017 papers，极市团队整理
DeblurGANv2
[ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang
deeplearningbook-chinese
Deep Learning Book Chinese Translation
Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》
Face-Hallucination-Benchmark
A collection of state-of-the-art face hallucination methods
GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
Jalali-Lab-Implementation-of-RAISR
Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.
SelfDeblur
Neural Blind Deconvolution Using Deep Priors (CVPR 2020)