Lucas Divine & Chris Lavin

Image Processing:

Critical to Hubble Discoveries

Lucas Divine & Chris Lavin

ECE 533: Image Processing

12/12/03

Welcome to the Hubble

The Hubble Space Telescope was deployed into space in 1990, but its conception took place in the 1940’s. The Hubble is a masterful telescope designed with long term space observation in mind. It has three cameras, two spectrographs, and many guidance sensors to capture high resolution images of astronomical objects. It is 43.5feet long, weighs 24.5 thousand pounds, and orbits the earth every 97 minutes1. It was deployed into low-Earth orbit, which is about 380 miles off the earth. This allows it to view space 10 times better than any telescopes on earth. It also allows the telescope to see wavelengths that the Earth’s atmosphere filters out. The Space Telescope Science Institute handles most of the research and day to day operations of the Hubble. It is operated for NASA and other Astronomical organizations. The images that the Hubble captures require a great deal of image processing before they are useful. This report will go over the various techniques used to create the incredible Hubble images that the public has been seeing over the last decade.

The Hubble’s Instruments

The Hubble Space Telescope has a variety of instruments to perform all of the necessary tasks that it needs to accomplish. Many of them allow for better image processing back on earth. There are many components designed to aid calibrating, focusing, and pointing the Hubble before any of the cameras can do their job. Software packages developed specifically for these purposes allow the control of the configuration hardware. Sensors allow for the reporting of the hundreds of parameters that the other components need to know. The Hubble is equipped with Fine Guidance Sensors (FGS) that are used for high speed photometry, astrometry, and pointing of the telescope. They provide feedback used to maneuver the telescope and perform celestial measurements.

The main interests of Image Processors are the cameras and other key instruments that directly affect the Images that the Hubble Space Telescope is able to take. They consist of the ACS, NICMOS, STIS, and WFPC22.

• ACS: This is the Advanced Camera for Surveys. It is a third generation Hubble Instrument that includes three electronic cameras, or channels, for varying pictures. Each of the three channels (wide field, high resolution, and solar blind) has a filter wheel or two that allow each channel todetect large swaths of light across a huge spectrum of wavelengths. For example, the ramp filters on the filter wheels allow narrow or medium band imaging centered at an arbitrary wavelength. The ACS increases the discovery efficiency of the Hubble by a factor of ten.

• NICMOS: The Near Infrared Camera and Multi Object Spectrometer sees the universe at near infrared wavelengths at a higher level of sensitively and in sharper detail than any other telescope. The infrared and near-infrared are the primary focus of the three cameras that make up the NICMOS.

• STIS: The Space Telescope Imaging Spectrograph provides spectra images at ultraviolet and visible wavelengths, probing our solar system as well as farther cosmological distances. Thus it acts like a prism to separate light into its component colors.It is a two dimensional spectrograph that blocks extraneous light and generates the spectra of many locations simultaneously.

• WFPC2: This is the current Wide Field Planetary Camera 2. There was a version 1, but the technology has been upgraded, and a version 3 is currently being developed. This camera is the workhorse behind many of the most famous Hubble pictures. The WFPC2 has a four-camera design that allows it to view more of the sky than a single camera would. A system of mirrors divides the beam of incoming light into four separate streams. This is the cause of sometimes ‘stair-step’ shape images. This devicecan observejust about anything and has over 48 filters.

Raw Imaging Data from the Hubble

Raw data is taken off the cameras and stored in the Hubble Data Archive3. When a user requests data from the Hubble Data Archive, the raw files are then calibrated by the On The Fly Reprocessing (OTFR) system.WFPC2, NICMOS, and STIS data can all be retrieved with the OTFR system. Through OTFR, Hubble archive users obtain data that can be reprocessed with the latest calibration files, up to date headers, and the latest software. This allows the system to only store uncalibrated data, which significantly reduces the storage space.

Even after recalibration and transmission to earth, the images themselves need a lot of work. Depending on what a specific scientist is looking for, they perform a variety of techniques on the Hubble pictures to take a closer look at what they are interested in.

Techniques for Manipulating Hubble Images

Most of the cosmos image detection depends on wavelengths. The spectrum set up to detect is very important in detecting what a scientist might be looking for or at. Creating a color spectrum and other spectra related processing techniques take center stage in the handling of Hubble Space Telescope raw images. Cosmic cleaning, image restoration, finding specifics in a picture, and finding the ‘whole picture’ from a few images are all tasks that need to be completed countless times on raw Hubble images. Almost any technique that allows you to focus on what you would like from the images is acceptable, and we cover some of the majority of techniques here.

Some effects are very specific to the Hubble Imaging, such as effects from temperature and positioning. There are many very specific software programs developed to address such issues. These programs are written by groups that work with particular areas of the Hubble imaging. Most other effects can be applied to the images via popular image processing software packages. These are used by the majority of people for the techniques necessary for observing what they wish.

Smoothing

Most Scientists use smoothing generally to reduce noise in images. This is very important because noise in astronomical images can sometimes mask the focus point of the experiment or investigation. The smoothing itself generally is accomplished by an averaging or low pass filter in the spatial domain. Various filtering happens in the majority of image processing techniques. The box, weighted average, and median filters all could be used to varying degrees in the smoothing process.

A specific example would be residual cosmic ray contaminations4. Cosmic rays can dilute images with clutter that is not needed. By smoothing with a median filter, almost all cosmic ray residuals can be removed from the pictures. This is done because the pixels surrounding the residual are used in determining the median to get rid of the ‘cosmic ray’ noise. At times, some other information in the image may be lost as well. This might be faint stars or objects that the filter thinks are noise. The loss of a specific important piece of information in a picture causes the trial of a different smoothing filter.

Image Restoration

“The HST image restoration problem is aggravated by insufficient image sampling, by a mixture of noise sources including spatially non-stationary, non-Gaussian noise, and by the desire to quantitatively evaluate the restored data5.”Due to these many effects that can affect Hubble images, a number of image restoration techniques have been developed and tried. Just about all work better on the Hubble Images than images taken from earth because atmospheric blurring changes continuously. Image Restoration is a large field, and by studying astronomy, many scientists can narrow down the total types of noise models that could be applied to the image. This allows them to develop their own type of ‘toolbox’ of image restoration methods to first use on a raw Hubble image.

In particular, frequency domain filtering, maximum-entropy methods, and Wiener filtering are especially useful in astronomical image restoration. Most spatial domain filters do not perform as well as frequency ones in detecting small details and enhancing them. In an example from ‘Digital Image Processing,’6 the Butterworth bandreject frequency domain filter is shown to apply to NASA images quite well. These filters restore small details and textures to images. Wiener filtering applies to the noise and degradation of images and is also used largely in image restoration.

Another reliable and effective restoration method for Hubble images is the Richardson-Lucy Iteration7. This method uses Poisson statistics as well as a convolution of the image to reproduce the image. The process is then repeated iteratively until the results converge to the maximum likelihood solution. Images restored using the Richardson-Lucy iteration have good photometric linearity and can be used for quantitative analysis.One downside is possible amplification of noise in an image, so running a noise reduction filter in the beginning helps take care of this problem.

Image restoration of Hubble images is also currently a large research field. New algorithms continue to be developed in this area as fast or faster than in other image processing areas.

Brightness and Contrast

The brightness of an object in space can generally tell you the distance it is away. This makes brightness in Hubble images of chief concern to mathematicians and scientists. Brightness is a subjective term, but in astronomical calculations the word is sometimes used when referring to luminance or radiance.

Contrast is used to balance out the light and darks to provide a greater level of detail in images. One of the hardest parts in dealing with astronomical images is the unique nature of light that comes from them. Most are extremely faint and very low contrast.

These and color adjustments are often the most used when publicizing images because they affect how aesthetically pleasing they look in books, journals, and news articles. At some point, almost every Hubble image has the brightness and contrast adjusted on them.

Area of Interest

Often when researchers are trying to obtain details from the Hubble’s results the preceding image processing techniques are simply not enough. The following are several of the most used techniques to extract particular information that astronomers are looking for.

Edge Detection:

Many of the items that researchers want to look at often incredibly small in comparison to the entire Hubble image. Often these areas of interest can be fifty by fifty pixels or smaller. One of the tools astronomers use to help distinguish detail, and in particular changing details is edge detection. Edge detection is normally accomplished by using various masks to find particular kinds of edges, there are numerous algorithms available to find the most important edges. One resent example8 of edge detection is research on one of Saturn’s moons, Titan. Scientists wanted to understand cloud movement on the moon. The images they had of the moon were incredibly small so they used edge detection to draw out the clouds and then compared changes over time. Otherwise they would never have been able to see the detail necessary to track the movements.

Subtraction:

When researchers want to find subtle changes in subsequent pictures taken by the Hubble, they can use subtraction to extract just the changes. This technique has been used to study the changes in Saturn’s cloud coverage. Essentially the pixel values of two images are subtracted from each other. In areas where there is no change you will only have black. In areas of change though, you should see be able to see the movements. In the case of Saturn researchers were able to follow a storm, which could only be seen in infrared; from creation to the point to where it went to the other side of the planet.

Sharpening:

Not surprisingly many images require sharpening before researchers can interpret an area of detail. Recently a group was able to discover the first brown dwarf (in between a planet and a star size wise) by using image sharpening. The brown dwarfs are incredibly small in comparison to a star. To confirm the existence of the brown dwarf9 researchers had to use a modified image sharpening technique to detect the shadow created by the dwarf.

Many of the original Hubble images were very blurred, sharpening was essential to get anything worthwhile out of the telescope. Dealing with the early Hubble images greatly increased our knowledge in sharpening blurred images. Granted the blur pattern was already known, but it still taught researchers a great deal.

Erosion and Dilation:

When you take an image of space you are most likely going to get a lot of stars in the picture. Often researchers aren’t interested in the thousands of tiny points of light in these images. They want to study one or two larger stars, that the Hubble was most likely focused on. The scientist can isolate these important stars by using erosion. Depending on the size of the area of interest a filter size is chosen and applied to the entire image. Only the stars that are large enough in the image will remain. Then the researcher simply applies the same filter with Dilation to return the detail to the remaining stars. Many of the images that are shown to the public have this process done to them.

MEM and MLM:

Although the image processes that are almost universally applied to Hubble images normally provide for better pictures they can often take important details away. It has been discovered that smoothing function that is used on Hubble images has the tendency to alter the brightness levels of nearby pixels. These changes are minor, but can still significantly affect results. To counter act the affects deconvolution of the image is performed using Maximum Entropy Method or Maximum Likelihood Method.

Building a Complete Image

Most of the Hubble Images the public sees come in nice rectangular form. But on occasion NASA releases images with odd staircase like empty spaces in the top right corner of the image. These images are the result of the WFPC2 camera which is actually four cameras10. Three of the four cameras take the standard images people are use to seeing. The top right camera though actual magnifies the area it is looking at to provide more detail. Often a scientist will only be interested in the picture taken by just one camera, but when they want an image of the entire area these four images have to be placed together. Image processing is used to first reduce the size of magnifying camera’s image so it matches the scale of the other images. Then computers are used to overlay the four images. In the following example you can get a feel for what is done.

Adding in 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 Color

It is possible to add color to just a single gray scale image, you just assign a particular color for each of the 256 gray values. The example below shows the crab nebula, to draw out the detail scientists in what is as much art as science choose a color spectrum and imposed it over the original brightness intensity image. We created the color image below by using a simple matlab program.

Natural Color

The Hubble is capable of seeing the majority of the light spectrum, but rarely does it take a picture of the entire spectrum, using filters the Hubble captures just a small piece of that spectrum. The images that we get are actually just depictions of the intensity of the filtered light. For most images astronomers first decide what kind of image they want to get. The most obvious is try to get a natural looking image, for example taking a picture of Mars (as seen below). This image was created by taking three separate images; one of red light, one of green light, and finally one of blue light. These three intensity images where combined to form an RGB image. This technique uses the fact that RGB images are actually stored as three separate intensity images representing the three primary colors.