image processing and pattern recognition
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Updated
Aug 17, 2017 - Python
image processing and pattern recognition
Clustering, Image Denoising, Manifold Learning and Autoencoders
FC-AIDE: Fully Convolutional Pixel Adaptive Image Denoiser
Use Gibbs sampling and variational inference to denoise the image and use EM to segment the image
PyTorch Implementation of image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections (NIPS 2016)
PyTorch implementation of Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP 2017)
A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
Code of Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
PyTorch implementation of S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction (2018)
Deep Boosting for Image Denoising in ECCV 2018 and its Real-world Extension in IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality assessment Guided image De-noising Network
PyTorch Implementation of "Densely Connected Hierarchical Network for Image Denoising", CVPRW, NTIRE2019
PyTorch implementation of Deep Convolution Networks for Compression Artifacts Reduction (ICCV 2015)
color image denoising using LinearRegression
Pytorch Implementation of "Deep Iterative Down-Up CNN for Image Denoising".
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