Date of Award
Master of Science
Electrical and Computer Engr
LWIR imaging in military applications is often plagued by dust degraded visuals while deployed in the field. The vast majority of current dust enhancement systems correct degradation through the application of algorithms applied to an entire frame. This method of applying the algorithm to the entire frame makes the implementation simple but does have a few negatives. For example, global application of the algorithm inherently enhances clear sections of an image as well as the dust occluded regions. This often produces unwanted artifacts and could possibly result in false targeting. This thesis proposes to address those unwanted effects through a combination of algorithmic and embedded systems development to create a dust quantification method for LWIR imagers. By doing so, this thesis will attempt to answer the question of whether it is possible to develop a system that detects dust locally and dynamically for further use in real-time image enhancement applications.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Underwood, Bryan Andrew, "Real-Time Obscurant Quantification in Long-Wave Infrared Imaging" (2021). Electronic Theses and Dissertations. 2210.