Image Processing: Critical to Hubble Discoveries Presented by Chris Lavin and Lucas Divine

The Hubble Space Telescope (HST) HST program run by Space Telescope Science Institute for NASA Quick Facts: Deployed 1990 Orbits Earth in 87 minutes 24.5 Thousand Pounds 10 times better than Telescopes on Earth Employs 6 primary instruments for astronomical image acquisition

Hubble Instruments Observatory - Handles calibration and cross-instrument issues Advanced Camera for Surveys – Long distance observation Fine Guidance Sensors – High speed measurements and pointing of the telescope Near Infrared Camera and Multi Object Spectrometer – Specializes in near-infrared wavelengths Space Telescope Imaging Spectrograph – Breaks images up into component colors like a prism Wide Field Planetary Camera 2 – The ‘Workhorse’ Camera

Raw Data from Hubble Hardware and software calibration in the On The Fly Reprocessing System (OTFR) Raw Data held in the Hubble Archive for researchers and scientists Stored in a compressed format and reprocessed when accessed

Image Processing Techniques Smoothing: Noise reduction through filtering in astronomical images is very important for specific information retrieval Image Restoration: Almost all Hubble images have had some unpredictable effects influence them Image restoration uses frequency domain filters to remove the effects of these influences

Image Processing Techniques 2 Brightness: Showing the correct brightness (luminance) in an image is highly important because distances are calculated many times based on it Contrast: Balancing of light and darks for strengthening of details, also helps make the image better looking to the human eye

Image Processing Techniques 3 Edge Detection: To extract areas of fine detail, Edge Detection is often used. Many different filters and programs are used. Subtraction: To detect movement in time lapsed images, Subtraction is used to see just the changes.

Building a Complete Image HST actually takes four images One magnified. To combine Scale Magnified Find overlap in each image merge

Adding Color The often beautiful images that we see on the news from the Hubble don't start out with all of those amazing colors. The Hubble actually only takes gray scale images, so typically the images that the public is presented with are created from several pictures.

Single Image Coloring Color can be added to an intensity image by simply picking a color for each gray scale value. The following was created using Matlab.

Natural Color The HST can take three intensity images using a Red Filter, a Green Filter, and a Blue Filter The three images are then used in an RGB Image

Representative Color The HST can take three intensity images using a in the infrared spectrum The three images are then used in an RGB Image Infrared Image of Saturn Natural Color Image of Saturn

Enhanced Color Particular areas of color are taken Each Color represents an atom R = Singly-ionized sulfur atoms G = Hydrogen B = Doubly- ionized oxygen atoms

Conclusion Image processing techniques that are developed for the Hubble generally get applied in other disciplines as well Image processing is critical to Hubble discoveries because it allows for the key information extraction within the image