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SYDE 575: Introduction to Image Processing Spatial-Frequency Domain: Implementations Textbook: Chapter 4.

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Presentation on theme: "SYDE 575: Introduction to Image Processing Spatial-Frequency Domain: Implementations Textbook: Chapter 4."— Presentation transcript:

1 SYDE 575: Introduction to Image Processing Spatial-Frequency Domain: Implementations Textbook: Chapter 4

2 Filtering in Spatial and Spatial- Frequency Domains Basic spatial filtering is essentially 2D discrete convolution between an image f and filter function h Convolution in spatial domain becomes multiplication in frequency domain

3 Spatial-Frequency Implementations We will discuss these implementations: Low pass filters: ideal, Butterworth, Gaussian High pass filters: ideal, Butterworth, Gaussian Edge enhancement: high boost filtering HVS modelling: Difference of Gaussians (DoG), Gabor, Laplacian of Gaussian Periodic noise filtering (Section 5.4)

4 Blurring/Noise reduction Noise characterized by sharp transitions in image intensity Such transitions contribute significantly to high frequency components of Fourier transform Intuitively, attenuating certain high frequency components result in blurring and reduction of image noise

5 Ideal LPF Cuts off all high-frequency components at a distance greater than a certain distance from origin (D 0 : cutoff frequency)

6 Visualization Source: Gonzalez and Woods

7 Effect of Different Cutoff Frequencies Source: Gonzalez and Woods

8 Effect of Different Cutoff Frequencies Source: Gonzalez and Woods

9 Effect of Different Cutoff Frequencies As cutoff frequency decreases  Image becomes more blurred  Noise becomes more reduced  Analogous to larger spatial filter sizes Noticeable ringing artifacts that increase as the amount of high frequency components removed is increased Why ringing?

10 Why is there ringing? Ideal low-pass filter function is a rectangular function The inverse Fourier transform of a rectangular function is a sinc function Convolution of a sinc and a step function generates ringing on both sides of the edge

11 Ringing Source: Gonzalez and Woods

12 Butterworth LPF Transfer function does not have sharp discontinuity establishing cutoff between passed and filtered frequencies Cutoff frequency D 0 defines point at which H(u,v)=0.5 Similar to exponential LPF

13 Butterworth LPF Source: Gonzalez and Woods

14 Spatial Representations Source: Gonzalez and Woods Tradeoff between amount of smoothing and ringing

15 Butterworth LPFs of Different Orders Source: Gonzalez and Woods

16 Gaussian LPF This is another form of a Gaussian filter, as used by Gonzalez & Woods (textbook) Transfer function is smooth, like Butterworth filter Gaussian in frequency domain remains a Gaussian in spatial domain Advantage: No ringing artifacts

17 Gaussian LPF Source: Gonzalez and Woods

18 Gaussian LPF Source: Gonzalez and Woods

19 Spatial-Frequency High Pass Filters (HPFs) HPFs are effectively the opposite of LPFs High pass filtering in the spatial-frequency domain is related to low pass filtering H HP (u,v) = 1 – H LP (u,v) h HP (x,y) =  (x,y) – h LP (x,y) Note: DC gain is zero for a HPF

20 Impact of High Pass Filtering Edges and fine detail characterized by sharp transitions in image intensity Such transitions contribute significantly to high frequency components of Fourier transform Intuitively, attenuating low frequency components and preserving high frequency components will retain image intensity edges

21 HPF Transfer Functions Ideal HPF Butterworth HPF Gaussian HPF

22 HPF Transfer Functions

23 Spatial Representations of HPFs

24 Ideal HPF Filtering

25 Butterworth HPF Filtering

26 Gaussian HPF Filtering

27 Observations of HPFs As with ideal LPF, ideal HPF shows significant ringing artifacts Second-order Butterworth HPF shows sharp edges with minor ringing artifacts Gaussian HPF shows good sharpness in edges with no ringing artifacts

28 Spatial-Frequency Edge Enhancement Edge enhancement can be performed directly in the spatial-frequency domain Example: high boost filtering (unsharp masking)

29 High frequency emphasis Advantageous to accentuate enhancements made by high-frequency components of image in certain situations (e.g., image visualization) Solution: multiply high-pass filter by a constant and add offset so zero frequency term not eliminated g(x,y) = f(x,y) + k g HPF (x,y) As discussed earlier, this is referred to as high-boost filtering

30 High Boost Filtering In spatial domain: g(x,y) = f(x,y) + k g HPF (x,y) Impulse response: h(x,y) =  (x,y) + k h HPF (x,y) Transfer function in spatial-frequency domain: H(u,v) = 1 + k H HPF (u,v) or: H(u,v) = 1 + kH HPF (u,v) = 1 + k(1- H LPF (u,v)) = (1+k) - kH LPF (u,v) Recall: Set k=1 for unsharp masking

31 Results Source: Gonzalez and Woods

32 Examples of Frequency Domain Filtering Source: Gonzalez and Woods

33 Human Visual System Models For a generic spatial-frequency image enhancement filter, what should the transfer function look like? 1)DC gain is typically reduced so 0 1 for frequency range where signal dominates Sketch:

34 Model of HVS Light entering the eye is processed by two steps 1)Cornea/Lens H 1 (u): modelled as LPF e.g., Gaussian 2)Retina H 2 (u): modelled as edge enhancement e.g., 1-Laplacian Combined: H(u) = H 1 (u) H 2 (u) = (1+(2  u/a) 2 ) e -2  2 u 2  2 Sketch:

35 Difference of Gaussians There are a number of Gaussian-based functions that mimic lateral inhibition Difference of Gaussians takes the difference of two Gaussians with different  H(u) = A e -2  2 u 2   2 - Be -2  2 u 2   2 With A>B and  1 <<  2 Sketch in frequency and time domains Can vary  1 and  2 to create filter bank with varying peak frequencies

36 Gabor Filter Gabor is a Gaussian band pass filter H(u) = (A/2) e -2  2 u 2   2 * [  (u-u p ) +  (u+u p )] In time domain, a Gaussian-modulated sinusoid (real part of Gabor filter) h(x) = A/(  (2  ) 0.5 ) e -0.5(x/  ) 2 cos(2  u p x) Sketch in frequency and time Similar shape as Difference of Gaussians, but with ringing Note: complex form of filter used for texture feature extraction

37 Laplacian of a Gaussian Consider the Marr-Hildreth operator i.e., a Laplacian of a Gaussian H(u) = (-j2  u) 2 e -2  2 u 2  2 = 4  2 u 2 e -2  2 u 2  2 Sketch in time and frequency domains What is the impact of this filter? Why?

38 Periodic Noise Reduction Typically occurs from electrical or electromechanical interference during image acquisition Spatially dependent noise Example: spatial sinusoidal noise

39 Example Source: Gonzalez and Woods

40 Observations Symmetric pairs of bright spots appear in the Fourier spectra Why?  Fourier transform of cosine function is the sum of a pair of impulse functions cos(2  u 0 x) 0.5[  (u + u 0 ) +  (u – u 0 )] Intuitively, sinusoidal noise can be reduced by attenuating these bright spots

41 Bandreject Filters Removes or attenuates a band of frequencies about the origin of the Fourier transform Sinusoidal noise may be reduced by filtering the band of frequencies upon which the bright spots associated with period noise appear

42 Example: Ideal Bandreject Filters Ideal bandreject filter

43 Example Source: Gonzalez and Woods

44 Notch Reject Filters Idea:  Sinusoidal noise appears as bright spots in Fourier spectra  Reject frequencies in predefined neighborhoods about a center frequency  In this case, center notch reject filters around frequencies coinciding with the bright spots

45 Some Notch Reject Filters Source: Gonzalez and Woods

46 Example: Moire pattern reduction Source: Gonzalez and Woods

47 Homomorphic Filtering Image can be modeled as a product of illumination (i) and reflectance (r) Unlike additive noise, can not operate on frequency components of illumination and reflectance separately

48 Homomorphic Filtering Idea: What if we take the logarithm of the image? Now the frequency components of i and r can be operated on separately

49 Homomorphic Filtering Framework Source: Gonzalez and Woods

50 Homomorphic Filtering: Image Enhancement Simultaneous dynamic range compression (reduce illumination variation) and contrast enhancement (increase reflectance variation) Illumination component characterized by slow spatial variations (low spatial frequencies) Reflectance component characterized by abrupt spatial variations (high spatial frequencies)

51 Homomorphic Filtering: Image Enhancement Can be accomplished using a high frequency emphasis filter in log space  DC gain of 0.5 (reduce illumination variations)  High frequency gain of 2 (increase reflectance variations) Output of homomorphic filter

52 Example Source: Gonzalez and Woods

53 Homomorphic Filtering: Noise Reduction Multiplicative noise model signalnoise Transforming into log space turns multiplicative noise to additive noise Low-pass filtering can now be applied to reduce noise

54 Example Source: Jernigan, 2003

55 Example Source: Jernigan, 2003


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