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Images: 2D signals

Module by: Robert Nowak. E-mail the author

Summary: This module introduces image processing, 2D convolution, 2D sampling and 2D FTs.

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Image Processing

Figure 1: Images are 2D functions fxy f x y
Figure 1 (figure1.png)

Linear Shift Invariant Systems

Figure 2
Figure 2 (figure2.png)

H is LSI if H α1 f1 xy+ α2 f2 xy=H f1 xy+H f2 xy H α1 f1 x y α2 f2 x y H f1 x y H f2 x y for all images f1 f1, f2 f2 and scalar Hfx x0 y y0 =gx x0 y y0 H f x x0 y y0 g x x0 y y0 LSI systems are expressed mathematically as 2D convolutions: gxy=hxαyβfαβdαdβ g x y β α h x α y β f α β where hxy h x y is the 2D impulse response (also called the "point spread function")

2D Fourier Analysis

uv=fxye(iux)e(ivy)dxdy u v y x f x y u x v y where is 2DFT, u and v is freq variables in xu x u and yv y v

2D Complex Exponentials are Eigenfunctions for 2D LSI Systems hxαyβei u0 αei v0 βdαdβ β α h x α y β u0 α v0 β change of variables α =xα α x α , β =yβ β y β hxαyβei u0 αei v0 βdαdβ=h α β ei u0 (x α )ei v0 (y β )d α d β β α h x α y β u0 α v0 β β α h α β u0 x α v0 y β where h α β ei u0 (x α )ei v0 (y β )d α d β =ei u0 xei v0 yh α β e(i u0 α )e(i v0 β )d α d β β α h α β u0 x α v0 y β u0 x v0 y β α h α β u0 α v0 β and h α β e(i u0 α )e(i v0 β )d α d β H u0 v0 β α h α β u0 α v0 β H u0 v0 H u0 v0 H u0 v0 is 2D Fourier transform of hxy h x y evaluated at freq u0 , v0 , u0 v0

Figure 3
Figure 3 (figure3.png)

gxy=hxy*fxy=hxαyβfαβdαdβ g x y h x y f x y β α h x α y β f α β Guv=Huvuv G u v H u v u v gxy=12π2Guveiuxeivydudv g x y 1 2 2 v u G u v u x v y The above equation is inverse 2DFT

2D Sampling Theory

Figure 4: Think of the image as a 2D surface
Figure 4 (figure4.png)

We can "sample" the height of the surface using a 2D impulse array.

Figure 5: Impulses spaced Δx Δ x apart in horizontal direction and Δy Δ y in vertical
Figure 5 (figure5.png)

fs xy=sxyfxy fs x y s x y f x y where fs xy fs x y is sampled image ... in frequency

2D FT of sy s y is a 2D impulse array in frequency suv s u v

Figure 6
Figure 6 (figure6.png)

mult. in time ⇔ convolution in freq Fs uv=Suvuv Fs u v S u v u v

Figure 7: uv u v is bandlimited in horizontal and vertical directions
Figure 7 (figure7.png)
Figure 8: periodically replicated in u,v , u v freq. plane
Figure 8 (figure8.png)

Nyquist Theorem

Assume that fxy f x y is bandlimited to ± Bx ± Bx , ± By ± By :

Figure 9
Figure 9 (figure9.png)

If we sample fxy f x y at spacings of Δx<π Bx Δ x Bx and Δy<π By Δ y By , then fxy f x y can be perfectly recovered from the samples by lowpass filtering.

Figure 10: ideal lowpass filter, 1 inside rectangle, 0 outside
Figure 10 (figure10.png)
Figure 11
Aliasing in 2D
(a) (b)
Figure 11(a) (figure11-1.png)Figure 11(b) (figure11-2.png)

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