tianjiaozeng's Stars
ysnan/NBD_KerUnc
Project page of the paper 'Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution' (CVPR 2020)
eriklindernoren/PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
siddiquesalman/flatnet
This is the official pytorch code repo for the ICCV 2019 paper Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images and the TPAMI 2020 paper FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements
weiaicunzai/pytorch-cifar100
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
1M50RRY/resnet18-preact
https://arxiv.org/pdf/1603.05027.pdf resnet18 with pre-activation implementation
uoguelph-mlrg/Cutout
2.56%, 15.20%, 1.30% on CIFAR10, CIFAR100, and SVHN https://arxiv.org/abs/1708.04552
csdwren/SelfDeblur
Neural Blind Deconvolution Using Deep Priors (CVPR 2020)
Po-Hsun-Su/pytorch-ssim
pytorch structural similarity (SSIM) loss
JamesGlare/Neural-Net-LabView-DLL
Deep Learning library in Labview. C++-based implementation of a feed-forward neural network. Compilation requires version 3.3.5. of the Eigen library. Additional layer-sharing, GAN and Mixture Density Capability to deal with ill-posed inverse problems. Currently applied to inverse-holography (infer back on the hologram from the light field it creates). Compiled with Visual Studio C++ 2015.
xinario/awesome-gan-for-medical-imaging
Awesome GAN for Medical Imaging
CSIPlab/learn-reference-pr
Official implementation of paper: Solving Phase Retrieval with a Learned Reference
JamesGlare/Holo_gen_models
Holographic wave-shaping has found numerous applications across the physical sciences, especially since the development of digital spatial-light modulators (SLMs). A key challenge in digital holog- raphy consists in finding optimal hologram patterns which transform the incoming laser beam into desired shapes in a conjugate optical plane. The existing repertoire of approaches to solve this inverse problem is built on iterative phase-retrieval algorithms, which do not take optical aberrations and deviations from theoretical models into account. Here, we adopt a physics-free, data-driven, and probabilistic approach to the problem. Using deep conditional Generative-Adversarial-Networks (cGAN) and conditional Variational Autoencoder (cVAE) architectures, we approximate posterior distributions of holograms for a given target laser intensity pattern. In order to reduce the cardinality of the problem, we train our models on a proxy mapping relating an 8 × 8-matrix of complex-valued spatial-frequency coefficients to the ensuing 100 × 100-shaped intensity distribution recorded on a camera. We discuss the degree of ’ill-posedness’ that remains in this reduced problem and challenge our generative models to find holograms that reconstruct given intensity patterns. Finally, we study the ability of the models to generalise to synthetic target intensities, where the existence of matching holograms cannot be guaranteed. We devise a forward-interpolating training scheme aimed at provid- ing models the ability to interpolate in laser intensity space, rather than hologram space and show that this indeed enhances model performance on synthetic data sets.
LIVIAETS/semi_curriculum
Code for our arxiv preprint: https://arxiv.org/abs/1904.05236
richzhang/PerceptualSimilarity
LPIPS metric. pip install lpips
itsermo/holovideo-mit
Holographic Video Rendering from Object-based Media Group at MIT Media Lab
zhanghang1989/ResNeSt
ResNeSt: Split-Attention Networks
Waller-Lab/LenslessLearning
Code for Lensless Learning Paper
Zhengqi-Wu/Unrolled-optimization-with-deep-priors
lalonderodney/SegCaps
Official Implementation of the Paper "Capsules for Object Segmentation".
jianzhangcs/ISTA-Net
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing, CVPR2018 (Tensorflow Code)
Waller-Lab/DiffuserCam
DiffuserCam Processing Code
cszn/IRCNN
Learning Deep CNN Denoiser Prior for Image Restoration (CVPR, 2017) (Matlab)
tianjiaozeng/ISTA-Net
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing (Tensorflow Code)
tianjiaozeng/awesome-matlab
A curated list of awesome Matlab frameworks, libraries and software.