Image Enhancement research document.pptx

Image Enhancement Research Document Presentation slides for MS IT Students that are intristing to do research on Image on the Image Enhancement.

1. Image enhancement Presented to: Dr. safiullah

2. Contents

3. SAIRA Ejaz 23015956-017

4. What is Image Enhancement? Image enhancement  is the process of making images more useful (such as making images more visually appealing, bringing out specific features, removing noise from images and highlighting interesting details in images).

5. Techniques Spatial domain Frequency domain

6. Spatial domain techniques:  manipulates the pixels of an image directly. This process happens in the image’s coordinate system, also known as the spatial domain. Frequency domain techniques: transforms an image from the spatial domain to the frequency domain. In this process, Mathematical transformations (such as the Fourier transform) are used. The image can be modified by manipulating its frequency components.

7. Most spatial domain enhancement operations can be reduced to the form  g (x, y) = T[ f (x, y)]  where  f (x, y)  is the input image,  g (x, y)  is the processed image and  T  is some operator defined over some neighbourhood of  (x, y).  For example- operation T(say, addition of 5 to all the pixel) is carried out in I( x,y ) which means that each pixel value is increased by 5. where, I’( x,y ) is the new intensity after adding 5 to I( x,y )

8. Point Processing Techniques Negative Images 2.    Thresholding 3.    Some of The Grey Level Transformations

9. Negative Images:  Negative images are useful for enhancing details. s = intensity_max — r

10. Thresholding     Thresholding transformations are useful for segmentation in which we want to isolate an object of interest from a background.   If thresholding is too low, image contains higher intensity values more. If thresholding is too high, image contains lower intensity values more.

11. Hijab zainab 23015956-004

12. Some Of Grey Level Transformations:   Linear Negative/Identity Logarithmic L og/Inverse log Power law nth power/ nth root

13. Identity: E ach value of the input image is directly mapped to the corresponding value of the output image. That results in the same input image and output image.   

14. Logarithmic: The general form of the log transformation is  The log transformation maps a narrow range of low input grey level values into a wider range of output values. The inverse log transformation performs the opposite transformation. Log functions are particularly useful when the input grey level values may have an extremely large range of values.

15. Power Law (Gamma Correction): Power law transformations have the following form : Map a narrow range of dark input values into a wider range of output values or vice versa. Varying γ gives a whole family of curves.

16. Power Law:

17. Piecewise Linear Transformation Functions : Contrast Stretching or Compression Stretch gray-level ranges where we desire more information (slope > 1) Compress gray-level ranges that are of little interest (0 < slope < 1)

18. Grey Level Slicing : Highlights a specific range of grey levels, other levels can be suppressed or maintained .

19. Bit Plane Slicing : By isolating particular bits of the pixel values in an image we can highlight interesting aspects of that image. Higher-order bits usually contain most of the significant visual information. Lower-order bits contain subtle details.

20. Bit Plane Slicing :

21. Salva sadiq 23015956-014

22. Histogram Equalization : Histogram equalization is a technique used to improve the contrast of an image by redistributing its pixel intensity values. Goal: To achieve a uniform distribution of intensities, thereby enhancing the overall contrast of the image. A graphical representation of the distribution of pixel intensity values in an image. X-axis represents the intensity values (from 0 to 255 for an 8-bit image) Y-axis represents the number of pixels with each intensity value.

23. Steps in Histogram E qualization : Calculate Histogram: Compute the histogram of the input image. Compute Cumulative Distribution Function (CDF): Calculate the cumulative distribution function for the histogram. Normalize CDF: Scale the CDF to match the intensity range (0 to 255 for an 8-bit image). Map Intensity Values: Use the normalized CDF to remap the intensity values of the input image, resulting in the equalized image.

24. Example : Consider this 3x3 grayscale image with intensity values ranging from 0-255 Original Image: [ [52, 55, 61], [59, 79, 61], [76, 61, 79] ]

25. Example : Step 1: First, we count number of pixels with each intensity value and calculate probability distribution function(pdf) Intensity | no of pixels ( nk ) | PDF= nk /n 52 | 1 | 0.11 55 | 1 | 0.11 59 | 1 | 0.11 61 | 3 | 0.33 76 | 1 | 0.11 79 | 2 | 0.22 Here n=sum of pixels which is 9

26. Example : Step 2: Compute the Cumulative Distribution Function (CDF) Next, we calculate the cumulative sum of the histogram values: Intensity | nk | PDF |CDF 52 | 1 | 0.11 |0.11 55 | 1 | 0.11 | 0.22 59 | 1 | 0.11 |0.33 61 | 3 | 0.33 |0.66 76 | 1 | 0.11 |0.77 79 | 2 | 0.22 |0.99

27. Example : Step 3: Normalize the CDF Normalize the CDF to the range [0, 255]. Intensity | nk | PDF |CDF |CDF X 255 52 | 1 | 0.11 |0.11 | 28.05 ≈ 28 55 | 1 | 0.11 | 0.22 | 56.1 ≈ 56 59 | 1 | 0.11 |0.33 | 84.15 ≈84 61 | 3 | 0.33 |0.66 | 168.3 ≈168 76 | 1 | 0.11 |0.77 | 196.35 ≈ 196 79 | 2 | 0.22 |0.99 | 252.45 ≈ 253

28. Example : Step 4: Map the Intensity Values Original | Equalized 52 | 28 55 | 32 59 | 56 61 | 168 76 | 196 79 | 253 SO input image is [ [52, 55, 61], [59, 79, 61], [76, 61, 79] ] output image is [ [ 28, 32, 168], [56, 253, 168], [ 196, 168, 253] ]

29. Properties : Histograms clustered at the low end correspond to dark images. Histograms clustered at the high end correspond to bright images.

30. Properties : Histograms with small spread correspond to low contrast images (i.e., mostly dark, mostly bright, or mostly gray). Histograms with wide spread correspond to high contrast images.

31. Applications and Future Trends : Artificial Intelligence and Deep Learning Real-Time Processing High Dynamic Range (HDR) Imaging Adaptive and Context-Aware Enhancement Super-Resolution Imaging Integration with Augmented Reality (AR) and Virtual Reality (VR) Ethical and Responsible Enhancement

32. THANKYOU !