# SYDE 575: Introduction to Image Processing

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

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

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)

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

Ideal LPF Cuts off all high-frequency components at a distance greater than a certain distance from origin (D0: cutoff frequency)

Visualization Source: Gonzalez and Woods

Effect of Different Cutoff Frequencies
Source: Gonzalez and Woods

Effect of Different Cutoff Frequencies
Source: Gonzalez and Woods

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?

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

Ringing Source: Gonzalez and Woods

Butterworth LPF Transfer function does not have sharp discontinuity establishing cutoff between passed and filtered frequencies Cutoff frequency D0 defines point at which H(u,v)=0.5 Similar to exponential LPF

Butterworth LPF Source: Gonzalez and Woods

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

Butterworth LPFs of Different Orders
Source: Gonzalez and Woods

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

Gaussian LPF Source: Gonzalez and Woods

Gaussian LPF Source: Gonzalez and Woods

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 HHP(u,v) = 1 – HLP(u,v) hHP(x,y) = d(x,y) – hLP (x,y) Note: DC gain is zero for a HPF Handwritten notes 3-18 to 3-19

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

HPF Transfer Functions
Ideal HPF Butterworth HPF Gaussian HPF

HPF Transfer Functions

Spatial Representations of HPFs

Ideal HPF Filtering

Butterworth HPF Filtering

Gaussian HPF Filtering

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

Spatial-Frequency Edge Enhancement
Edge enhancement can be performed directly in the spatial-frequency domain Example: high boost filtering (unsharp masking) pp to 3-19 in handwritten notes

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 gHPF(x,y) As discussed earlier, this is referred to as high-boost filtering

High Boost Filtering In spatial domain: g(x,y) = f(x,y) + k gHPF(x,y)
Impulse response: h(x,y) = d(x,y) + k hHPF(x,y) Transfer function in spatial-frequency domain: H(u,v) = 1 + k HHPF(u,v) or: H(u,v) = 1 + kHHPF(u,v) = 1 + k(1- HLPF(u,v)) = (1+k) - kHLPF(u,v) Recall: Set k=1 for unsharp masking k=1 gives unsharp masking

Results Source: Gonzalez and Woods
Result of using 1-Laplacian for enhancement in the frequency domain. Source: Gonzalez and Woods

Examples of Frequency Domain Filtering
Source: Gonzalez and Woods

Human Visual System Models
For a generic spatial-frequency image enhancement filter, what should the transfer function look like? DC gain is typically reduced so 0<H(u)<1 H(u) approaches zero as u increases H(u) > 1 for frequency range where signal dominates Sketch:

Model of HVS Light entering the eye is processed by two steps
Cornea/Lens H1(u): modelled as LPF e.g., Gaussian Retina H2(u): modelled as edge enhancement e.g., 1-Laplacian Combined: H(u) = H1(u) H2(u) = (1+(2pu/a)2) e-2p2u2s2 Sketch: See notes 3-22 to calculate u of peak response

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 s H(u) = A e-2p2u2s12 - Be-2p2u2s22 With A>B and s1<< s2 Sketch in frequency and time domains Can vary s1 and s2 to create filter bank with varying peak frequencies

Gabor Filter Gabor is a Gaussian band pass filter
H(u) = (A/2) e-2p2u2s12 * [ d(u-up) + d(u+up)] In time domain, a Gaussian-modulated sinusoid (real part of Gabor filter) h(x) = A/(s(2p)0.5) e-0.5(x/s)2cos(2pupx) Sketch in frequency and time Similar shape as Difference of Gaussians, but with ringing Note: complex form of filter used for texture feature extraction

Laplacian of a Gaussian
Consider the Marr-Hildreth operator i.e., a Laplacian of a Gaussian H(u) = (-j2pu)2 e-2p2u2s2 = 4p2u2 e-2p2u2s2 Sketch in time and frequency domains What is the impact of this filter? Why? Add detail

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

Example Source: Gonzalez and Woods

cos(2pu0x) <-> 0.5[d(u + u0) + d(u – u0)]
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(2pu0x) <-> 0.5[d(u + u0) + d(u – u0)] Intuitively, sinusoidal noise can be reduced by attenuating these bright spots

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

Example: Ideal Bandreject Filters

Example Source: Gonzalez and Woods

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

Some Notch Reject Filters
Source: Gonzalez and Woods

Example: Moire pattern reduction
In physics, a moiré pattern is an interference pattern created, for example, when two grids are overlaid at an angle, or when they have slightly different mesh sizes. Source: Gonzalez and Woods

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 Handwritten notes 3-29 to 3-34

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

Homomorphic Filtering Framework
Source: Gonzalez and Woods

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)

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

Example Source: Gonzalez and Woods

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

Example Source: Jernigan, 2003

Example Source: Jernigan, 2003

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