⛄ Differentiable Manifold Reconstruction for Point Cloud Denoising (ACM MM 2020)

Deep Learning on Image Denoising: An overview (Neural Networks, 2020)

This is the official implementation of state-of-the-art medical image denoising method titled as "Dynamic Residual Attention Network (DRAN)".

3D image denoising using a modified U-Net architecture that exploits a prior image. Models are trained using efficient tensorflow pipeline based on keras and tf.data.Dataset API

We derive a fundamental property of the posterior distribution in Gaussian denoising, and use it to propose a new way for uncertainty visualization, which requires no training or fine-tuning.

Implementing CVPR 2020 paper "ROBUST AND INTERPRETABLE BLIND IMAGE DENOISING VIA BIAS - FREE CONVOLUTIONAL NEURAL NETWORKS"

This repository provides a PyTorch implementation of FFDNet image denoising https://arxiv.org/abs/1710.04026. First implemented by Matias Tassano https://doi.org/10.5201/ipol.2019.231, the FFDNet source code has been adapted so as to extract cameras PRNU.

This repository contains implementation of a QPSK-based telecommunication system optimized using deep learning based image compression and denoising in LabVIEW Communications environment using Python and Keras.

Dual Path Denoising Network for Real Photographic Noise

Using CNN to de noise images.

Use a ResNet to denoise the noisy images

Implementation of Denoising Diffusion Probabilistic Model from Scratch for Image Generation Task in PyTorch

deep convolutional autoencoder for image denoising

I built a Denoising Autoencoder to remove noise from the image. Image Denoising is the process of removing noise from the Images The noise present in the images may be caused by various intrinsic or extrinsic conditions which are practically hard to deal with. The problem of Image Denoising is a very fundamental challenge in the domain of Image …

Une série de notebooks qui expliquent en détail comment fonctionnent les modèles de diffusion