Open AccessA Blind Blur Detection Method for Electro-optic (EO) Images
Author(s) -
Greg Bower
Publication year - 2018
Publication title -
proceedings of the annual conference of the prognostics and health management society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.18
H-Index - 11
ISSN - 2325-0178
DOI - 10.36001/phmconf.2018.v10i1.351
Subject(s) - payload (computing) , computer vision , artificial intelligence , computer science , gaussian , image (mathematics) , gaussian blur , sequence (biology) , image restoration , image processing , physics , computer network , quantum mechanics , network packet , biology , genetics
Blurring in Electro-Optic (EO) images is a significant issue that can arise due to the payload and platform operations. It would be advantageous for unmanned platforms to determine if significant blurring is present within captured images before the images are observed and the collection sequence has ended. In this way, the degradation can be identified and remedied in operation in real-time. In this paper, we demonstrate that a statistical algorithm called Symbolic Analysis (SA) is suitable for detecting blurring in the output images of EO systems. The SA algorithm adapted from previous work is described and demonstrated on an example image with artificial Gaussian-based blurring induced.