# 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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2007Theo Schouten1 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

2007Theo Schouten2 Noise functions

2007Theo Schouten4 Mars, mariner 6

2007Theo Schouten5 Linear degradation When the degradation process is linear: H( k 1 f 1 + k 2 f 2 ) = k 1 H( f 1 ) + k 2 H( f 2 ) 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.

2007Theo Schouten6 Position invariant, inverse filtering When 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 up pseudo-inverse filter: use only parts of H(u,v)

2007Theo Schouten7 Degradation function by experiment

2007Theo Schouten8 by modelling H(u,v)= exp( -k(u 2 +v 2 ) 5/6 ) atmosferic turbulence model

2007Theo Schouten9 by calculation, linear motion Suppose a movement of the image during shutter opening: g(x,y) =  0 T f(x-x 0 (t),y-y 0 (t)) dt G(u,v)=   [  0 T f(x-x 0 (t),y-y 0 (t)) dt ] e -j2  (ux+vy) dxdy = F(u,v)  0 T e -j2  [uxo(t)+vy0(t)] dt = F(u,v) H(u,v) With linear motion x 0 (t)=at/T and y 0 (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

2007Theo Schouten10 Linear motion blur

2007Theo Schouten11 Pseudo-inverse filter

2007Theo Schouten12 Gaussian movement A 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

2007Theo Schouten13 with noise Uniform 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

2007Theo Schouten14 Wiener filtering minimum mean square error: e 2 = E{ (f-f c ) 2 } F c (u,v) =[1/H(u,v)] [ |H(u,v| 2 / (|H(u,v| 2 +S  (u,v)/S f (u,v))] G(u,v) S  (u,v) = |N(u,v)| 2 power spectrum of noise Approximations of S  (u,v)/S f (u,v): K (constant)  |P(u,v)| 2 (power spectrum of Laplacian)  found by iterative method to minimize e 2 (constrained least squares filtering)

2007Theo Schouten15 Example Wiener filter Original Noise added Pseudo-inverse Wiener filter

2007Theo Schouten16 Linear motion Wiener filter

2007Theo Schouten17 Geometric distorsion 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.

2007Theo Schouten18 Corrections Original Nearest neighbor Bilinear interpolation More complex, slower: bilinear interpolation subsampling e.g. 5x5

2007Theo Schouten19 Calibration Calibration, e.g. x’ = a +b x +c y and y’ = r +s x +t y : affine transformations Also higher order terms like d x 2 + e y 2 + f xy

2007Theo Schouten20 Fish eye lens x' = x + x*(K 1 *r 2 + K 2 *r 4 + K 3 *r 6 ) + P 1 *(r 2 + 2*x 2 ) + 2*P 2 *x*y y' = y + y*(K 1 *r 2 + K 2 *r 4 + K 3 *r 6 ) + P 2 *(r 2 + 2*y 2 ) + 2*P 1 *x*y

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