Motion Blur Detection Using Convolutional Neural Network

  • R. B. PreethamEmail author
  • A. Thyagaraja Murthy
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)


In this paper, we identify movement obscure from a solitary, hazy picture. We propose a profound learning way to deal with and anticipate the likelihood dissemination of movement obscure at the fix level by utilizing a Convolutional Neural Network (CNN). The design we follow will moved toward the issue by cutting 100 pictures into 30 × 3 0 fixes and connected our movement obscure calculation to them (with an irregular rate of half). At that point named the hazy and non-foggy patches with 1 s (0 for still, 1 for hazy), and stacked the adjusted pictures as our preparation information. In this Paper, we aim to estimate blurred motion from a single blurry image and propose an in-depth learning approach to predict probabilistic patch level movement blur distribution using a Convolutional Neural Network (CNN).


Convolutional Neural Network Motion deblur Python NumPy OpenCV 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of E&CSri Jayachamarajendra College of Engineering, JSS Science and Technology UniversityMysuruIndia

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