2007 Theo Schouten 1 Restoration With Image Restoration one tries to repair errors or distortions in an image, caused during the image creation process. In general our starting point is a degradation and noise model: g(x,y) = H ( f(x,y) ) + (x,y) Determined by quality of equipment and image taking conditions: •image restoration is computationally complex •equipment as degradation free as possible •seen technical and financial limitations •medical: low radiation, little time in magnet-tube
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2007Theo Schouten1 Restoration With Image Restoration one tries to repair errors or distortions in an image, caused during the image creation process.
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2007 Theo Schouten 1
Restoration
With Image Restoration one tries to repair errors or distortions in an image, caused during the image creation process.
In general our starting point is a degradation and noise model: g(x,y) = H ( f(x,y) ) + (x,y)
Determined by quality of equipment and image taking conditions:•image restoration is computationally complex•equipment as degradation free as possible
•seen technical and financial limitations•medical: low radiation, little time in magnet-tube
•lowest image quality to achieve medical goals•web-cams: cheap lens distortions corrected by CPU in cam
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Noise functions
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Only noise
See also enhancement, mean and median filters
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Mars, mariner 6
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Linear degradationWhen the degradation process is linear:
H( k1f1 + k2f2 ) = k1 H( f1) + k2 H( f2 )
we can write (we temporarily leave the noise out of consideration):
g(x,y) = H(f(x,y))= H( f(,) (x- ,y- ) d d )= f(,) H( (x- ,y- ) ) d d = f(,) h(x, ,y, ) d d
h(x, ,y, ) is the "impulse response" or "point spread function",the degraded image of an ideal light point.The integral is called the "superposition“ or "Fredholm” integral of the first kind.
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Position invariant, inverse filteringWhen H is a spatial invariant:
Hf(x- ,y- )=g(x- ,y- ) then:
h(x, ,y, ) = h(x- ,y- )and
g(x,y) = f(,) h(x- ,y- ) d d
a convolution integral, and taking into account the noise:
G(u,v) = H(u,v)F(u,v) + N(u,v)
Inverse filtering:
G(u,v)/H(u,v) = F(u,v) + N(u,v)/H(u,v)Problems:
if H(u,v) = 0, or small: noise is blown uppseudo-inverse filter: use only parts of H(u,v)
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Degradation function by experiment
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by modelling
H(u,v)= exp(-k(u2+v2)5/6 )
atmosfericturbulencemodel
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by calculation, linear motionSuppose a movement of the image during shutter opening:g(x,y) = 0
T f(x-x0(t),y-y0(t)) dt
G(u,v) = [0T f(x-x0(t),y-y0(t)) dt ] e -j2(ux+vy) dxdy
= F(u,v) 0T e -j2[uxo(t)+vy0(t)] dt
= F(u,v) H(u,v)
With linear motion x0(t)=at/T and y0(t)=bt/T :
H(u,v) = {T/[ (ua+vb)] } sin[ (ua+vb)] e -j[ua+vb]
This has a lot of 0’s : (ua+vb) = n (any integer)pseudo-inverse filter is useless
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Linear motion blur
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Pseudo-inverse filter
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Gaussian movementA 1-D Gaussian kernel for distortions in the horizontal direction. The intensity of each pixel is spread out over the neighboring pixels according to this kernel.
Power spectrum
Inverse filter
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with noiseUniform noise [0,1] added (rounding floating point to unsigned byte)
Movement lines disappear due to noise
Inverse filter: nothing
Pseudo-inverse filter, only when H(u,v) > T
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Wiener filteringminimum mean square error: e2 = E{ (f-fc)2}
Lenses often show a typical pincushion or barrel deviation.When the projection function x'=g(x) is known, for each measured pixel it can be determined from which parts of ideal pixels it is buit up.If the inverse function g-1 is known, then for each ideal pixel we can determine from which parts of the distorted pixels it is built up of.
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Corrections
Original Nearest neighbor Bilinear interpolation
More complex, slower:•bilinear interpolation•subsampling e.g. 5x5
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Calibration
Calibration, e.g.x’ = a +b x +c y and y’ = r +s x +t y : affine transformations