1. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Fourier Convolution Outline  Review linear imaging model  Instrument response function vs Point spread function  Convolution integrals Fourier Convolution Reciprocal space and the Modulation transfer function  Optical transfer function  Examples of convolutions  Fourier filtering  Deconvolution  Example from imaging lab Optimal inverse filters and noise

2. 22.058 - lecture 4, Convolution and Fourier Convolution Instrument Response Function The Instrument Response Function is a conditional mapping, the form of the map depends on the point that is being mapped. This is often given the symbol h(r|r’). Of course we want the entire output from the whole object function, and so we need to know the IRF at all points.

3. 22.058 - lecture 4, Convolution and Fourier Convolution Space Invariance Now in addition to every point being mapped independently onto the detector, imaging that the form of the mapping does not vary over space (is independent of r 0 ). Such a mapping is called isoplantic. For this case the instrument response function is not conditional. The Point Spread Function (PSF) is a spatially invariant approximation of the IRF.

4. 22.058 - lecture 4, Convolution and Fourier Convolution Space Invariance Since the Point Spread Function describes the same blurring over the entire sample, The image may be described as a convolution, or,

5. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integrals Let’s look at some examples of convolution integrals, So there are four steps in calculating a convolution integral: #1. Fold h(x’) about the line x’=0 #2. Displace h(x’) by x #3. Multiply h(x-x’) * g(x’) #4. Integrate

6. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integrals Consider the following two functions: #1. Fold h(x’) about the line x’=0 #2. Displace h(x’) by x x

7. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integrals

8. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integrals Consider the following two functions:

9. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integrals

10. 22.058 - lecture 4, Convolution and Fourier Convolution Some Properties of the Convolution commutative: associative: multiple convolutions can be carried out in any order. distributive:

11. 22.058 - lecture 4, Convolution and Fourier Convolution Recall that we defined the convolution integral as, One of the most central results of Fourier Theory is the convolution theorem (also called the Wiener-Khitchine theorem. where, Convolution Integral

12. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Theorem In other words, convolution in real space is equivalent to multiplication in reciprocal space.

13. 22.058 - lecture 4, Convolution and Fourier Convolution Convolution Integral Example We saw previously that the convolution of two top-hat functions (with the same widths) is a triangle function. Given this, what is the Fourier transform of the triangle function? = ?

14. 22.058 - lecture 4, Convolution and Fourier Convolution Proof of the Convolution Theorem The inverse FT of f(x) is, and the FT of the shifted g(x), that is g(x’-x)

15. 22.058 - lecture 4, Convolution and Fourier Convolution Proof of the Convolution Theorem So we can rewrite the convolution integral, as, change the order of integration and extract a delta function,

16. 22.058 - lecture 4, Convolution and Fourier Convolution Proof of the Convolution Theorem or, Integration over the delta function selects out the k’=k value.

17. 22.058 - lecture 4, Convolution and Fourier Convolution Proof of the Convolution Theorem This is written as an inverse Fourier transformation. A Fourier transform of both sides yields the desired result.

18. 22.058 - lecture 4, Convolution and Fourier Convolution Fourier Convolution

19. 22.058 - lecture 4, Convolution and Fourier Convolution Reciprocal Space real space reciprocal space

20. 22.058 - lecture 4, Convolution and Fourier Convolution Filtering We can change the information content in the image by manipulating the information in reciprocal space. Weighting function in k-space.

21. 22.058 - lecture 4, Convolution and Fourier Convolution Filtering We can also emphasis the high frequency components. Weighting function in k-space.

22. 22.058 - lecture 4, Convolution and Fourier Convolution Modulation transfer function

23. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens

24. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens

25. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens

26. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens 2D FT

27. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens Projections

28. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with lens Projections

29. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with pinhole

30. 22.058 - lecture 4, Convolution and Fourier Convolution

31. 2D FT

32. 22.058 - lecture 4, Convolution and Fourier Convolution Optics with pinhole Projections

33. 22.058 - lecture 4, Convolution and Fourier Convolution Projections

34. 22.058 - lecture 4, Convolution and Fourier Convolution Deconvolution to determine MTF of Pinhole

35. 22.058 - lecture 4, Convolution and Fourier Convolution FT to determine PSF of Pinhole

36. 22.058 - lecture 4, Convolution and Fourier Convolution Filtered FT to determine PSF of Pinhole