Denoising images with Fourier Ring Correlation loss

Last update: Mar 12, 2022

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Overview

Denoising images with Fourier Ring Correlation loss

The python code accompanies the working manuscript Image quality measurements and denoising using Fourier Ring Correlations. The main code is used for training denoising networks.

In the notebooks folder all the images shown in the manuscript are generated using jupyter notebooks. The data and network models used in the notebooks are deposited in Zenodo repository

Examples of training FRC neural networks, and additional processing with FRC will be added in the next days.

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Denoising images with Fourier Ring Correlation loss

Related tags

Overview

Denoising images with Fourier Ring Correlation loss

Owner

Task-related Saliency Network For Few-shot learning

Camera calibration & 3D pose estimation tools for AcinoSet

An adaptive hierarchical energy management strategy for hybrid electric vehicles

PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

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Serverless proxy for Spark cluster

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Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

PN-Net a neural field-based framework for depth estimation from single-view RGB images.

Wide Residual Networks (WideResNets) in PyTorch

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

An interactive DNN Model deployed on web that predicts the chance of heart failure for a patient with an accuracy of 98%

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This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

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