Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Overview

Camera Condition Monitoring and Readjustment by means of Noise and Blur

This repository contains the source code of the paper: Wischow, M., Gallego, G., Ernst, I., Börner, A., (2021). Camera Condition Monitoring and Readjustment by means of Noise and Blur.

We propose a modular and general self-health-maintenance framework that strives for optimal application performance. We demonstrate the working principle of the framework on the exemplary application of object detection, and focus on motion blur and noise as typical undesired disturbances (see Fig. 1).

Wischow_fig1

General information

  • Each sub-directory contains its own readMe file with instructions.
  • All python scripts that are intended to be executable have a commented out example code at the end. Before you run any scripts, please uncomment and customize the respective code blocks first. Matlab scripts can be customized and executed directly.
  • All python package requirements are specified in the tested versions. Attention: The tensorflow packages of the blur estimation and the noise estimation CNNs are not compatible with each other! Please consider two separate package environments for both.

Citation

@misc{wischow2021camera,
      title={Camera Condition Monitoring and Readjustment by means of Noise and Blur}, 
      author={Maik Wischow and Guillermo Gallego and Ines Ernst and Anko Börner},
      year={2021},
      eprint={2112.05456},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

Vladislav Kurenkov 4 Dec 14, 2021
Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021

Introduction Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021 Prerequisites Python 3.8 and conda, get Conda CUDA 11

51 Dec 03, 2022
Unofficial implementation of Pix2SEQ

Unofficial-Pix2seq: A Language Modeling Framework for Object Detection Unofficial implementation of Pix2SEQ. Please use this code with causion. Many i

159 Dec 12, 2022
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)

FCOS: Fully Convolutional One-Stage Object Detection This project hosts the code for implementing the FCOS algorithm for object detection, as presente

Tian Zhi 3.1k Jan 05, 2023
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion.

OstrichRL This is the repository accompanying the paper OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion. It contain

Vittorio La Barbera 51 Nov 17, 2022
Alphabetical Letter Recognition

BayeesNetworks-Image-Classification Alphabetical Letter Recognition In these demo we are using "Bayees Networks" Our database is composed by Learning

Mohammed Firass 4 Nov 30, 2021
Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

16 Nov 19, 2022
[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion Project Website; Datasets li

Jinpeng Wang 114 Oct 16, 2022
Tutorial in Python targeted at Epidemiologists. Will discuss the basics of analysis in Python 3

Python-for-Epidemiologists This repository is an introduction to epidemiology analyses in Python. Additionally, the tutorials for my library zEpid are

Paul Zivich 120 Nov 17, 2022
A code generator from ONNX to PyTorch code

onnx-pytorch Generating pytorch code from ONNX. Currently support onnx==1.9.0 and torch==1.8.1. Installation From PyPI pip install onnx-pytorch From

Wenhao Hu 94 Jan 06, 2023
Unified tracking framework with a single appearance model

Paper: Do different tracking tasks require different appearance model? [ArXiv] (comming soon) [Project Page] (comming soon) UniTrack is a simple and U

ZhongdaoWang 300 Dec 24, 2022
Pytorch implementation of AREL

Status: Archive (code is provided as-is, no updates expected) Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement

8 Nov 25, 2022
A lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look At CoefficienTs)

Real-time Instance Segmentation and Lane Detection This is a lane detection integrated Real-time Instance Segmentation based on YOLACT (You Only Look

Jin 4 Dec 30, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb

Yu Tian 117 Jan 03, 2023
Codes for AAAI22 paper "Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum"

Paper For more details, please see our paper Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum which has been accepted a

14 Sep 30, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

SANDS This is an annonymous repository containing code and data necessary to reproduce the results published in "Semi-supervised Stance Detection of T

2 Sep 22, 2022
Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.

Fast Training of Neural Lumigraph Representations using Meta Learning Project Page | Paper | Data Alexander W. Bergman, Petr Kellnhofer, Gordon Wetzst

Alex 39 Oct 08, 2022
SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article

Moore 34 Nov 03, 2022
Fast and Simple Neural Vocoder, the Multiband RNNMS

Multiband RNN_MS Fast and Simple vocoder, Multiband RNN_MS. Demo Quick training How to Use System Details Results References Demo ToDO: Link super gre

tarepan 5 Jan 11, 2022