1. Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli E-mail: m.g31_mesu@yahoo.com Fall 2012

2. Image Enhancement The principle Objective of enhancement is to process an image so that the result is more suitable than the original image for specific application Purpose  Image enhancement for human reception  Image Enhancement for machine automation Category  Spatial domain  Frequency domain

3. g(x,y)=T(f(x,y)) g(x,y,k)=T(f(x,y,k)) where g(x,y) is output image f(x,y) is input image x,y are spatial index k is temporal index Gray Level Transformation or point processing mm nn U[M×N] V[M×N]

4. Applying spatial domain filter (Sliding windows) AMAM

5. Fig 3.2 Gray Level Transformation a) contrast enhancement b) thresholding

6. Some Useful Gray level (intensity) Transformation

7. Log Transform

8. Image Negative Image intensity is in the range of [0, L-1] S=L-1-r Some Useful Gray level (intensity) Transformation Applying threshold

10. Image Negative

12. Gamma Correction

13. Gamma correction applied to forest picture(γ=0.5)

14. Gamma Correction

15. Histogram Processing  The histogram of a digital image with gray levels from 0 to L-1 is a discrete function h(r k )=n k, where:  r k is the kth gray level  n k is the Number of pixels in the image with that gray level  n is the total number of pixels in the image  k = 0, 1, 2, …, L-1  Normalized histogram: p(r k )=n k /n –sum of all components = 1

16. Dark image Bright image Histogram for different images

17. Low contrast image High contrast image Histogram for different images

18. Histogram Processing  The shape of the histogram of an image does provide useful info about the possibility for contrast enhancement.  Types of processing:  Histogram equalization  Histogram matching (specification)  Local enhancement

19. Histogram Equalization  Method Determine and Transformation function that seeks to produce an output image that has uniform histogram. S=T( r )0≤ r ≤1  Transformation Function a)T(r) is single-valued and monotonically increasing the interval 0≤ r ≤1 b)0≤T(r)≤1for 0≤ r ≤1

20. Histogram Equalization

21.  Histogram equalization(HE) results are similar to contrast stretching but offer the advantage of full automation, since HE automatically determines a transformation function to produce a new image with a uniform histogram.

23. Noise  Images are corrupted by random variations in intensity values called noise due to non-perfect camera acquisition or environmental conditions.  Assumptions: –Additive noise: a random value is added at each pixel –White noise: The value at a point is independent on the value at any other point.

24.  Salt and pepper noise  random occurrences of both black and white intensity values  are specified by noise density  Impulse noise:  random occurrences of white intensity values  are specified by noise density Common Types of Noise

25.  Gaussian noise:  impulse noise but its intensity values are drawn from a Gaussian distribution  models sensor noise (due to camera electronics)  are specified by noise mean and variance or dB

26. Examples of Noisy Images

27. Spatial Filtering

28. a=(m-1)/2 and b=(n-1)/2, m and n (odd numbers)  For x=0,1,…,M-1 and y=0,1,…,N-1  Also called convolution (primarily in the frequency domain)

29. Spatial Filtering  Another representation

30. Original Spatial Filtering

36.  Linear Filters is filtering in which the value of an output pixel is a linear combination of the values of the pixels in the input pixel’s neighborhood  Nonlinear Filter use pixel neighborhoods but do not explicitly use coefficients.

37. Spatial Filtering  Low-pass filters eliminate or attenuate high frequency components in the frequency domain (sharp image details), and result in image blurring.  High-pass filters attenuate or eliminate low-frequency components (resulting in sharpening edges and other sharp details).  Band-pass filters remove selected frequency regions between low and high frequencies (for image restoration, not enhancement).

38. Linear Smoothing Filters  Also are referred to  averaging filter  weighted averaging filter  lowpass filter  Results in  blurring (removal of small details prior to large object extraction, bridging small gaps in lines)  noise reduction.

39. Linear Smoothing Filters

42. Order Statistic Filters

43. Sharpening Filters  To highlight fine detail or to enhance blurred detail. –smoothing ~ integration –sharpening ~ differentiation  Categories of sharpening filters: –Derivative operators –Basic highpass spatial filtering –High-boost filtering

44. The Gradient  Non-isotropic  Its magnitude (often call the gradient) is isotropic  Computations is not trivial for whole image  Reducing Computation Overhead

45. The Gradient

46. The Uses of Gradient for Edge Detection

47. Digital Function Derivatives  First derivative: –0 in constant gray segments –Non-zero at the onset of steps or ramps –Non-zero along ramps –Produce thicker edges in an image  Second derivative: –0 in constant gray segments –Non-zero at the onset and end of steps or ramps –0 along ramps of constant slope. –Have stronger response to fine detail

48. Laplacian  Digital implementation:  Two definitions of Laplacian: one is the negative of the other  Accordingly, to recover background features: I: if the center of the mask is negative II: if the center of the mask is positive

49. Laplacian