Hans1984's repositories
material-illumination-geometry
Code for Siggraph 2021 paper: The effect of shape and illumination on material perception: model and applications.
Gamut-Extension
the project of color gamut extension
CBDNet
Code for "Toward Convolutional Blind Denoising of Real Photographs", CVPR 2019
colourlab
Colour space transforms and colour metrics for python
DANet
Dual Attention Network for Scene Segmentation (CVPR2019)
Deep-HdrReconstruction
Official PyTorch implementation of "Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss" (SIGGRAPH 2020)
DeepInverseRendering
Source code for our paper Deep Inverse Rendering for High-resolution SVBRDF Estimation from an Arbitrary Number of Images
GCNet
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Generative-Adversarial-Network-Tutorial
Tutorial on creating your own GAN in Tensorflow
Hans1984_old
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
hdrcnn
HDR image reconstruction from a single exposure using deep CNNs
ITM-CNN
This is the official repository for our paper ITM-CNN, presented in ACCV 2018.
Learning-to-See-in-the-Dark
Learning to See in the Dark. CVPR 2018
material-appearance-similarity
Code for the paper "A Similarity Measure for Material Appearance" presented in SIGGRAPH 2019 and published in ACM Transactions on Graphics (TOG).
NLRN
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
NNDemosaicAndDenoise
Joint demosaicing and denoising of RAW images with a CNN
Non-Local_Nets-Tensorflow
An implement of Non-local neural networks for tensorflow version
PReNet
Progressive Image Deraining Networks: A Better and Simpler Baseline (CVPR 2019)
SRGAN-tensorflow
Tensorflow implementation of the SRGAN algorithm for single image super-resolution
stylegan2-ada-pytorch
StyleGAN2-ADA - Official PyTorch implementation
tensorflow
Computation using data flow graphs for scalable machine learning
UNIR
Unsupervised Adversarial Image Reconstruction (UNIR)