1. Color Images

2. Color and the Human Eye Biophysical basis:
- The human eye contains 3 kinds photoreceptive cells (cone) cells that respond to different wavelengths:
“L”: longer wavelengths
“M”: medium wavelengths
“S”: shorter wavelengths
Source: wikipedia

3. Color and the Human Eye
When light hits the cones, an electrical signal is transmitted to the brain.
The relative intensity of the signal from the different types of cone cells is then mapped by the brain into what we interpret at color.
Example: Which cones would detect light emitted at 5500 Angstroms?
Which type of cone cell has the greatest response at this wavelength?
Source: wikipedia

4. Color and the Human Eye
What this means is that the eye effectively only “sees” three different primary colors. All other colors can be obtained by varying the relative intensities of these colors.
Source: wikipedia

5. Color and The Non-Human Eye
Not all types of animals have the same number of types of cones.
Humans: 3 types (color blindness normally due to 1 or more type not working)
Birds: 4 types (UV channel)
Bees: 3 types (see UV, but not red)
Most mammals (e.g. dogs): 2 types
From Dr. M. Nofziger’s notes on optics at the University of Arizona
Source: wikipedia

6. What dogs and cats can see
What Humans can see
What dogs and cats can see
What bulls can see

7. Making a Color Image
The net result is that to make a color image you need 3 images taken with different filters.
- More doesn’t help since the eye can only see three.
- A “true color” image is one that uses three filters that roughly
correspond to the transmission regions for the different types of cones.
- Johnson BVR filters are reasonably matched to S,M,L cones
(also close to BGR from CTO filter set)
Source: wikipedia

8. Below: red lines are wavelengths of peak throughput for BVR filters
Above: red lines are wavelengths of peak response for eye
Source: wikipedia

9. False Color
There’s no fundamental reason you have to map information in true color.
You can easily map and wavelength images to red, green, blue.

10. Kitakami River, Japan March 14, 2011 Jan 16, 2011 Green, red, ir
From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)
Green, red, ir

11. NGC 7635 Red: F658N(NII), Green: F656N(Halpha), Blue: F502N(OIII)
Red: F658N(NII), Green: F656N(Halpha), Blue: F502N(OIII)

12. RGB Color Image
RGB (which stands for Red-Green-Blue) is the most common format for storing color images.
- For each color, the image is renormalized to have integer values running from 0-255, where 0 is dark and 255 is maximum brightness.
> 16.6 million
yellow(255,255,0)
green(0,255,0)
cyan(0,255,255)
red(255,0,0)
blue(0,0,255)
white: (255,255,255)
black: (0,0,0)
Source: wikipedia
magenta(255,0,255)

13. RGB Color Image
The dynamic range has some practical implications.
1. Say that your input B-band image has pixel values from 0 to
a. If you remap such that 0->0 and >255, then you’ll only see the bright
stars (everything below 160 counts will -> 0).
b. If you instead define the maximum to be some lower value (say 1000->255),
then everything above this value will get mapped to and likely appear white if the object is bright in all three filters.
2. Astronomical images are generally (close to) linear. This may not be a desirable trait for making a color image. To increase the effective dynamical range (i.e. minimize the problem described above), it is common to apply a mathematical transform to the images before combining them into a color image.
a. Common types of transforms to apply are a square root or a logarithm.
i.e. sqrt(41000) = 202, sqrt(0)=0
log10(41000)= only works if all pixels >0 initially...

14. RGB Color Image Other things to keep in mind
The minimum pixel value (zero level). If you want to see the sky, this should be slightly below the mean sky level. If you don’t want to see it, then it should be slightly above.
Gamma value: The gamma value is an alternate way or remapping the intensity scaling instead of taking square roots or logarithms. The basic transformation is
response = (input intensity)gamma
so:
linear response: gamma=1
sqrt response gamma=0.5
In general, if you want to bring out faint features, use gamma<1. If you want to only see the bright objects, then use gamma>1.