Info

° « O

o"(x, y) k ® o"(x, y) s(x, y) -(k® o"{x, y))

Figure 19.2 Here the mechanics of iterative image estimation are laid out in plain sight.

The first column is the current estimate, the middle column the blurred version, and the right column the difference image. Note that the difference declines rapidly with successive iterations.

o"(x, y) k ® o"(x, y) s(x, y) -(k® o"{x, y))

Figure 19.2 Here the mechanics of iterative image estimation are laid out in plain sight.

The first column is the current estimate, the middle column the blurred version, and the right column the difference image. Note that the difference declines rapidly with successive iterations.

o'(x,y) = o(x, y) + (s(x, y)-(k® o(x, v))). (Equ. 19.6)

What does this messy equation mean? Basically, it implies that by performing the operations on the right side of the equation, we can compute a new version of the original image. In other words, we can find a new image, o'(x, y), by starting from o{x, y), the old version of the original image. To get it, we take the original image and to it add the difference between the CCD image and the convolution of the original image with the point-spread function. This looks like the chicken-and-egg problem all over again, but not quite.

It is reasonable to think that if this relationship is valid for the original image,

• R = 1 1=5 ' I . ' R = 1.3 /= 5 • I . ' Ft = 1.6 /=5 .'

• * . • « , « ff= 1 /= 10 .

Was this article helpful?

0 0
Telescopes Mastery

Telescopes Mastery

Through this ebook, you are going to learn what you will need to know all about the telescopes that can provide a fun and rewarding hobby for you and your family!

Get My Free Ebook


Post a comment