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RESTORATION AND DEGRADATION OF IMAGE MD. AHASANUZZAMAN & SANJAY SAHA

Image Degradation & Resoration

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RESTORATION AND DEGRADATION OF IMAGEMD. AHASANUZZAMAN & SANJAY SAHA

DEGRADATION OF IMAGE

Why? Imperfect imaging system

Imperfect transmission channel

Atmospheric conditions

Relative motion between object & camera

DEGRADATION OF IMAGE

Gaussian Noise

f(x,y) = H[g(x,y)] + ἠ(x,y) 3x3 convolving window

f(x,y) = ∑H(k,l)g(x-k,y-l)+ ἠ(x,y)k,l € w

RESTORATION OF IMAGE

Types Inverse Filtering

Wiener Filtering

Kalman Filtering

Algebraic Approach

Apriori

Blurring function

Noise statistics

IMPULSE NOISE EMBEDDED IMAGE (1/2)

Restoration from impulse noise embedded image Step 1: If the target pixel is noisy, go to step2. Else, go to the

next pixel

Step 2: Replace the noise pixel with a new value.

Local Window

Size: (2M + 1) x (2M + 1)

How to detect noise?

Difference between pixel values from the median of the image

IMPULSE NOISE EMBEDDED IMAGE (2/2)

This method will not work fine when the image is too much noisy The choice of local window may not reflect the global image.

Choice of small local window doesn’t even consider the local regional detail

Wang and Zhang

Two windows of the same size

Around two pixels

One is the target pixel which is noisy

Another is a non-noisy pixel

The non-noisy pixel is selected from a larger sets of candidate

MATHEMATICAL DESCRIPTION DEBLUR IMAGE

Shift-Invariant Model - every point in the original image spreads out the same way in forming the blurry image

Using the convolution Model –

f(x,y) = h(x,y)*g(x,y) + n(x,y)

Here,

g(x,y) = original image

f(x,y) = blurred image

h(x,y) = point spread function or blur function

n(x,y) = noise model

BLUR IMAGE RESTORATION (DEBLUR IMAGE)

How can we restore the original image ?

INVERSE FILTERING

Fast Fourier Transform and Inverse Fourier Transform give us the solution

Equation of Blur Image,

f(x,y) = h(x,y)*g(x,y) + n(x,y)

Using the Fourier Transformation- convolution can be written in multiplying the Fourier domain of the point spread function and original image

F(m,n) = H(m,n) × G(m,n)

G(m,n) = F(m,n) / H(m,n)

g(x,y) = Inverse Fourier (G(m,n))

SIMULATION IN MATLAB

SIMULATION IN MATLAB (CONT’D..)

SIMULATION IN MATLAB (CONT’D..)

SIMULATION IN MATLAB (CONT’D..)

SIMULATION IN MATLAB (CONT’D..)

Thank you!

Md. Ahasanuzzam & Sanjay Saha