Chen, H.Y., Teng, M.G., Shi, B.X., Wang, Y.Z. and Huang, T.J. (2022) A Residual Learning Approach to Deblur and Generate High Frame Rate Video With an Event Camera. IEEE Transactions on Multimedia, 1-14.
https://doi.org/10.1109/TMM.2022.3199556

has been cited by the following article:

• TITLE: Camera Independent Motion Deblurring in Videos Using Machine Learning

  AUTHORS: Tyler Welander, Ronald Marsh, Bryce Gruber

  KEYWORDS: Motion Blur, Video, Convolutional Neural Network, Long Short-Term Memory, AirSim, OpenCV

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.15 No.4, November 6, 2023

  ABSTRACT: In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is fully implemented in software and is not aware of any of the characteristics of the camera. We found a solution by implementing a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid model. Our CNN-LSTM is able to deblur video without any knowledge of the camera hardware. This allows it to be implemented on any system that allows the camera to be swapped out with any camera model with any physical characteristics.