Nathan Hara was born and raised on the island of Maui and graduated from Seabury Hall in 2007. He is currently majoring in Electrical Engineering at California Polytechnic State University, San Luis Obispo. Nathan is a member of the Renewable Energy Club and plans to work in the field of Sustainable Energy after graduation. When home, he likes to go to the beach and catch up with old friends.
Home Island: Maui
High School: Seabury Hall
Institute when accepted: California Polytechnic State University, San Luis Obispo
Analysis and Optimization of Daylight Telescope Images
Project Site: Pacific Defense Solutions
Mentor: Dave Schultz & Daron Nishimoto
Project Abstract:
The majority of images taken from telescopes occur at night when the contrast between the background and target object is at its greatest. The major issue with daylight images deals with light from the Sun and daytime sky saturating the image’s background, making it much brighter than the light produced from or reflected off of an object. To improve these images, we developed a program that enhances the contrast of daylight images using a method called frame differencing. Frame differencing subtracts one image of the object from another image of the same object, which is moved within the frame between the two exposures. The resultant image has greatly reduced background light and fixed-pattern noise, and preserves the object as both a positive and negative component due to the frame subtraction. The process is then repeated and the resulting frames are stacked and summed up in order to enhance the magnitude of the object’s light signal. We also used MATLAB to produce a graphical user interface (GUI) in which the user can select the directory containing the data to be analyzed. The program reads in the images depending on their filename extension and processes the images using the frame differencing method. The GUI also has the ability to automatically run through as many images as the directory contains, instead of manually stepping through the images one at a time. Future updates for this project would be to apply this method for real-time data analysis rather than stored data analysis.