Image Processing Topic

1. RESTORATION AND DEGRADATION OF IMAGE
MD. AHASANUZZAMAN & SANJAY SAHA

2. DEGRADATION OF IMAGE
 Why?
 Imperfect imaging system
 Imperfect transmission channel
 Atmospheric conditions
 Relative motion between object &
camera

3. DEGRADATION OF IMAGE
 Gaussian Noise
f(x,y) = H[g(x,y)] + ἠ(x,y)
 3x3 convolving window
f(x,y) = ΣH(k,l)g(x-k,y-l)+ ἠ(x,y)
k,l € w

4. RESTORATION OF IMAGE
 Types
 Inverse Filtering
 Wiener Filtering
 Kalman Filtering
 Algebraic Approach
 Apriori
 Blurring function
 Noise statistics

5. 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

6. 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

7. 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

8. BLUR IMAGE RESTORATION (DEBLUR IMAGE)
How can we restore the original image ?

9. 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))

10. SIMULATION IN MATLAB

11. SIMULATION IN MATLAB (CONT’D..)

12. SIMULATION IN MATLAB (CONT’D..)

13. SIMULATION IN MATLAB (CONT’D..)

14. SIMULATION IN MATLAB (CONT’D..)

15. Thank you!
Md. Ahasanuzzam & Sanjay Saha