0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
Intensity-Image Reconstruction Using Event Camera Data by Changing in LSTM Update
Authors :
Arezoo Rahmati Soltangholi
1
Ahad Harati
2
Abedin Vahedian
3
1- Department of Engineering,Graduated from Computer engineering, Ferdowsi University, Mashhad, Iran
2- Department of Engineering,Faculty of Computer engineering, Ferdowsi University, Mashhad, Iran
3- Department of Engineering,Faculty of Computer engineering, Ferdowsi University, Mashhad, Iran
Keywords :
event camera،intensity-image reconstruction،deep neural network
Abstract :
Event cameras offer many advantages, but their output is inherently ambiguous and needs to be converted into a more understandable output. One way to use the output of these cameras is to reconstruct the intensity. Various methods have been proposed for image reconstruction using event data, each attempting to improve image quality from specific aspects. In this study, we aim to increase image quality in a challenging condition when the number of events is very low or zero without retraining or changing the network structure during training. Another challenging situation is at the initial start-up moment which requires an initialization time. In this study, we used the potential of the E2VID model and increased the video quality without changing the trained model. Our method performs better than the E2VID method with an 11.9% improvement in the first 10 frames and a 2% improvement in entire videos in SSIM metric.
Papers List
List of archived papers
Intensity-Image Reconstruction Using Event Camera Data by Changing in LSTM Update
Arezoo Rahmati Soltangholi - Ahad Harati - Abedin Vahedian
Explainable Error Detection Method for Structured Data using HoloDetect framework
Abolfazl Mohajeri Khorasani - Sahar Ghassabi - Behshid Behkamal - Mostafa Milani
Robustness Scan of Digital Circuits Using Convolutional Neural Networks
Mobin Vaziri - Mohammad Mehdi Rahimifar - Hadi Jahanirad
FAST: FPGA Acceleration of Neural Networks Training
Alireza Borhani - Mohammad Hossein Goharinejad - Hamid Reza Zarandi
A Novel Hybrid Method for Clustering Text Documents using Evolutionary Optimization
Muhammad Naderi - Maryam Amiri
SingAll: Scalable Control Flow Checking for Multi-Process Embedded Systems
Mehdi Amininasab - Ahmad Patooghy - Mahdi Fazeli
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis
Elham Shabaninia - Fatemeh sadat Eslami - Ali Afkari Fahandari - Hossein Nezamabadi-pour
No-Reference Video Quality Assessment by Deep Feature Maps Relations
Amir Hossein Bakhtiari - Azadeh Mansouri
Pyramid Transformer for Traffic Sign Detection
Omid Nejati manzari - Amin Boudesh - Shahriar B. Shokouhi
A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment
Sajad Rezaei - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Mohammad-Amin Memar Kochebagh
more
Samin Hamayesh - Version 41.3.1