Image Processing - IDC

Image Processing - IDC

Image Processing in Freq. Domain Restoration / Enhancement Inverse Filtering Match Filtering / Pattern Detection Tomography Enhancement v.s. Restoration Image Enhancement: A process which aims to improve bad

images so they will look better. Image Restoration: A process which aims to invert known degradation operations applied to images. Enhancement vs. Restoration Better visual representation Remove effects of

sensing environment Subjective Objective No quantitative measures Mathematical, model dependent quantitative measures Typical Degradation Sources Low Illumination

Optical distortions (geometric, blurring) Sensor distortion (quantization, sampling, sensor noise, spectral Atmospheric attenuation sensitivity, de-mosaicing) (haze, turbulence, ) Image Preprocessing Restoration Enhancement Freq.

Domain Spatial Domain Filtering Point operations Spatial operations Denoising Inverse filtering Wiener filtering Examples

Hazing Echo image Motion Blur Blurred image Blurred image + additive white noise Reconstruction as an Inverse Problem n noise Original image Distortion

f g g H f n H Reconstruction Algorithm measurements f

?So what is the problem g H 1 g n f Typically: The distortion H is singular or ill-posed. The noise n is unknown, only its statistical properties can be learnt.

Key point: Stat. Prior of Natural Images likelihood prior MAP estimation: f arg max P f g arg max P g f P f x x Bayesian Reconstruction (MAP) From amongst all possible solutions, choose the one :that maximizes the a-posteriori probability

P(f | g)P(g | f) P(f) Most probable solution P(f) measurements P(g|f) Image space Bayesian Denoising Assume an additive noise model : g=f + n A MAP estimate for the original f: f arg max P f | g f

Using Bayes rule and taking the log likelihood : f arg max P g | f P f arg min log P g | f log P f f f P g Bayesian Denoising If noise component is white Gaussian distributed: g=f + n where n is distributed ~N(0,) f arg min g f 2 R f

f data term prior term R(f) is a penalty for non probable f Inverse Filtering Degradation model: g(x,y) = h(x,y)*f(x,y) G(u,v)=H(u,v)F(u,v) F(u,v)=G(u,v)/H(u,v)

Inverse Filtering (Cont.) Two problems with the above formulation: 1. 2. H(u,v) might be zero for some (u,v). In the presence of noise the noise might be amplified: F(u,v)=G(u,v)/H(u,v) + N(u,v)/H(u,v) Solution: Use prior information 2 F arg min HF G R F F

data term prior term Option 1: Prior Term Use penalty term that restrains high F values: F arg min E F F 2 E F HF G F 2 where Solution:

E F * 2 H HF G 2F 0 F * H F * G H H H (u, v) 1 F G H H (u, v) 1 F 0 Degraded Image (echo)

F=G/H F * H G * H H Degraded Image (echo+noise) F

* H G * H H The inverse filter is C(H)= H*/(H*H+ ) At some range of (u,v): S(u,v)/N(u,v) < 1 noise amplification. 18 16 14 12

C(H) 10 =10-3 8 6 4 2 0 -1

-0.5 0 0.5 H 1 1.5 2 Option 2: Prior Term 1. 2.

Natural images tend to have low energy at high frequencies White noise tend to have constant energy along freq. where F arg min E F F 2 2 2

E F HF G u v F 2 240 220 200 180 160 140 120 100 80

60 40 50 100 150 200 250 Solution: E F 2 H * HF G 2 u 2 v 2 F 0 F F

* H G * 2 2 H H u v This solution is known as the Wienner Filter Here we assume N(u,v) is constant.

If N(u,v) is not constant: * H F * G 2 2 H H u v N (u, v) Degraded Image (echo+noise) Wienner Filtering

Wienner Previous Degraded Image (blurred+noise) Inverse Filtering Using Prior (Option 1) Wienner Filtering Matched Filter in Freq. Domain Pattern Matching: Finding occurrences of a particular pattern in an image.

Pattern: Typically a 2D image fragment. Much smaller than the image. Image Similarity Measures Image Similarity Measure: A function that assigns a nonnegative real value to two given images. Small measure high similarity Preferable to be a metric distance (non-negative, identity, symmetric, triangular inequality) d( -

)0 Can be combined with thresholding: 1 d ( P, Q) threshold f ( P, Q ) 0 otherwise The Matching Approach Scan the entire image pixel by pixel. For each pixel, evaluate the similarity between its local neighborhood and the pattern.

The Euclidean Distance as a Similarity Measure Given: kk pattern P nn image I kxk window of image I located at x,y - Ix,y For each pixel (x,y), we compute the distance: 2 E d I , P x, y 1 2 k

k1 2 1 2 I x, y P k 2 I

x i , y j P i , j i , j 0

Complexity O(n2k2) FFT Implementation 2 E d I , P x, y k1 1 2 I x, y P k 2 I 2 x i, y j P 2 i, j 2 I x i, y j P i, j

i , j 0 I 2 * k k mask of 1's Fixed 2( I * P) Convolution can be applied rapidly using FFT. Complexity: O(n2 log n) Nave vs. FFT :Performance table for a 10241024 image, on a 1.8 GHz PC Time Complexity Space

Integer Arithmetic Nave FFT O(n 2 k 2 ) n2 O(n 2 log n) Yes Run time for 1616 .Sec 1.33

Run time for 3232 .Sec 4.86 Run time for 6464 .Sec 31.30 n2 No .Sec 3.5 .Sec 3.5 .Sec 3.5 7

x 10 2 1.5 1 0.5 0 Normalized Cross Correlation NCC: A similarity measure, based on a normalized crosscorrelation function. Maps two given images to [0,1] (absolute value).

Measures the angle between vectors Ix,y and P Invariant to intensity scale and offset. d NC I I , P x, y x, y I 1 P P1 I x , y I 1 P P1

Efficient Implementation Note that Thus, X d NC X 1 Y Y 1 X Y nXY I I , P x, y

x, y I x , y 1 P P1 I x , y I x , y 1 P P1 I x , y P k 2 I x , y P I 2 2 2 2

I k I P P k P x , y x, y x, y

The above expression can be implemented efficiently using 3 convolutions. 7 x 10 1.5 2 1.5 1 1

0.5 0.5 0 Euclidean distance similarity measure 0 NCC similarity measure 6 x 10 60

11 60 1.2 10 50 9 8 40 50 1 40

7 6 30 0.8 30 0.6 5 20 4 20

0.4 3 10 2 10 0.2 1 10 20 30

40 50 60 70 Euclidean distance similarity measure 10 20

30 40 50 60 70 NCC similarity measure Computer Tomography using FFT CT Scanners In 1901 W.C. Roentgen won the Nobel Prize (1st in physics)

for his discovery of X-rays. Wilhelm Conrad Rntgen CT Scanners In 1979 G. Hounsfield & A. Cormack, won the Nobel Prize for developing the computer tomography. The invention revolutionized medical imaging. Allan M. Cormack Godfrey N. Hounsfield Tomography: Reconstruction from Projection 1

f(x,y) 2 Projection & Sinogram Projection: All ray-sums in a direction Sinogram: collects all projections P(t) y t

x f(x,y) X-rays Sinogram t CT Image & Its Sinogram K. Thomenius & B. Roysam The Slice Theorem Fourier Transform

y v 1 1 x f(x,y) spatial domain u

frequency domain The Slice Theorem f(x,y) = object g(x) = projection of f(x,y) parallel to the y-axis: g(x) = f(x,y)dy Fourier Transform of f(x,y): F(u,v) = f(x,y) e -2i(ux+vy)

Fourier Transform at v=0 : F(u,0) = f(x,y) e -2iux = [ f(x,y)dy] e -2iux dxdy

dxdy dx = g(x) e -2iux dx = G(u) The 1D Fourier Transform of g(x) Interpolation Method Interpolate (linear, quadratic etc) in the frequency space to obtain all values in F(u,v). Perform Inverse Fourier Transform to obtain the image f(x,y). v F(u,v) u

THE END

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