Digital Image Processing Image Enhancement Point Processing 2
![Digital Image Processing Image Enhancement (Point Processing) Digital Image Processing Image Enhancement (Point Processing)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-1.jpg)
![2 of 45 Contents In this lecture we will look at image enhancement point 2 of 45 Contents In this lecture we will look at image enhancement point](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-2.jpg)
![3 of 45 Basic Spatial Domain Image Enhancement Most spatial domain enhancement operations can 3 of 45 Basic Spatial Domain Image Enhancement Most spatial domain enhancement operations can](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-3.jpg)
![4 of 45 Point Processing The simplest spatial domain operations occur when the neighbourhood 4 of 45 Point Processing The simplest spatial domain operations occur when the neighbourhood](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-4.jpg)
![Point Processing Example: Negative Images taken from Gonzalez & Woods, Digital Image Processing (2002) Point Processing Example: Negative Images taken from Gonzalez & Woods, Digital Image Processing (2002)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-5.jpg)
![Point Processing Example: Negative Images (cont…) 6 of 45 Original Image y Enhanced Image Point Processing Example: Negative Images (cont…) 6 of 45 Original Image y Enhanced Image](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-6.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 7 of 45 Point Images taken from Gonzalez & Woods, Digital Image Processing (2002) 7 of 45 Point](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-7.jpg)
![Point Processing Example: Thresholding (cont…) 8 of 45 Original Image y Image f (x, Point Processing Example: Thresholding (cont…) 8 of 45 Original Image y Image f (x,](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-8.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 9 of 45 Intensity Images taken from Gonzalez & Woods, Digital Image Processing (2002) 9 of 45 Intensity](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-9.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 10 of 45 Basic Images taken from Gonzalez & Woods, Digital Image Processing (2002) 10 of 45 Basic](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-10.jpg)
![11 of 45 Logarithmic Transformations The general form of the log transformation is s 11 of 45 Logarithmic Transformations The general form of the log transformation is s](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-11.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 12 of 45 Logarithmic Images taken from Gonzalez & Woods, Digital Image Processing (2002) 12 of 45 Logarithmic](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-12.jpg)
![13 of 45 Logarithmic Transformations (cont…) Original Image y Enhanced Image x Image f 13 of 45 Logarithmic Transformations (cont…) Original Image y Enhanced Image x Image f](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-13.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 14 of 45 Power Images taken from Gonzalez & Woods, Digital Image Processing (2002) 14 of 45 Power](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-14.jpg)
![15 of 45 Power Law Transformations (cont…) Original Image y Enhanced Image x y 15 of 45 Power Law Transformations (cont…) Original Image y Enhanced Image x y](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-15.jpg)
![16 of 45 Power Law Example 16 of 45 Power Law Example](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-16.jpg)
![17 of 45 Power Law Example (cont…) γ = 0. 6 17 of 45 Power Law Example (cont…) γ = 0. 6](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-17.jpg)
![18 of 45 Power Law Example (cont…) γ = 0. 4 18 of 45 Power Law Example (cont…) γ = 0. 4](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-18.jpg)
![19 of 45 Power Law Example (cont…) γ = 0. 3 19 of 45 Power Law Example (cont…) γ = 0. 3](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-19.jpg)
![Power Law Example (cont…) The images to the right show a magnetic resonance (MR) Power Law Example (cont…) The images to the right show a magnetic resonance (MR)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-20.jpg)
![21 of 45 Power Law Example 21 of 45 Power Law Example](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-21.jpg)
![22 of 45 Power Law Example (cont…) γ = 5. 0 22 of 45 Power Law Example (cont…) γ = 5. 0](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-22.jpg)
![Power Law Transformations (cont…) An aerial photo of a runway is shown This time Power Law Transformations (cont…) An aerial photo of a runway is shown This time](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-23.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 24 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 24 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-24.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 25 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 25 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-25.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 26 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 26 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-26.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 27 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 27 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-27.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 28 of 45 More Images taken from Gonzalez & Woods, Digital Image Processing (2002) 28 of 45 More](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-28.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 29 of 45 Piecewise Images taken from Gonzalez & Woods, Digital Image Processing (2002) 29 of 45 Piecewise](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-29.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 30 of 45 Gray Images taken from Gonzalez & Woods, Digital Image Processing (2002) 30 of 45 Gray](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-30.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 31 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 31 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-31.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 32 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 32 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-32.jpg)
![33 of 45 Bit Plane Slicing (cont…) 33 of 45 Bit Plane Slicing (cont…)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-33.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 34 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 34 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-34.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 35 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 35 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-35.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 36 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 36 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-36.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 37 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 37 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-37.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 38 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 38 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-38.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 39 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 39 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-39.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 40 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 40 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-40.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 41 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 41 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-41.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 42 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 42 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-42.jpg)
![Images taken from Gonzalez & Woods, Digital Image Processing (2002) 43 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 43 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-43.jpg)
![44 of 45 Report Histogram Specification 44 of 45 Report Histogram Specification](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-44.jpg)
![45 of 45 Summary We have looked at different kinds of point processing image 45 of 45 Summary We have looked at different kinds of point processing image](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-45.jpg)
- Slides: 45
![Digital Image Processing Image Enhancement Point Processing Digital Image Processing Image Enhancement (Point Processing)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-1.jpg)
Digital Image Processing Image Enhancement (Point Processing)
![2 of 45 Contents In this lecture we will look at image enhancement point 2 of 45 Contents In this lecture we will look at image enhancement point](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-2.jpg)
2 of 45 Contents In this lecture we will look at image enhancement point processing techniques: – What is point processing? – Negative images – Thresholding – Logarithmic transformation – Power law transforms – Grey level slicing – Bit plane slicing
![3 of 45 Basic Spatial Domain Image Enhancement Most spatial domain enhancement operations can 3 of 45 Basic Spatial Domain Image Enhancement Most spatial domain enhancement operations can](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-3.jpg)
3 of 45 Basic Spatial Domain Image Enhancement Most spatial domain enhancement operations can be reduced to the form Origin x g (x, y) = T[ f (x, y)] where f (x, y) is the input image, g (x, y) is the processed image (x, y) and T is some operator defined over some neighbourhood y Image f (x, y) of (x, y)
![4 of 45 Point Processing The simplest spatial domain operations occur when the neighbourhood 4 of 45 Point Processing The simplest spatial domain operations occur when the neighbourhood](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-4.jpg)
4 of 45 Point Processing The simplest spatial domain operations occur when the neighbourhood is simply the pixel itself In this case T is referred to as a grey level transformation function or a point processing operation Point processing operations take the form s=T(r) where s refers to the processed image pixel value and r refers to the original image pixel value
![Point Processing Example Negative Images taken from Gonzalez Woods Digital Image Processing 2002 Point Processing Example: Negative Images taken from Gonzalez & Woods, Digital Image Processing (2002)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-5.jpg)
Point Processing Example: Negative Images taken from Gonzalez & Woods, Digital Image Processing (2002) 5 of 45 Negative images are useful for enhancing white or grey detail embedded in dark regions of an image – Note how much clearer the tissue is in the negative image of the mammogram below Original Image s = 1. 0 - r Negative Image
![Point Processing Example Negative Images cont 6 of 45 Original Image y Enhanced Image Point Processing Example: Negative Images (cont…) 6 of 45 Original Image y Enhanced Image](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-6.jpg)
Point Processing Example: Negative Images (cont…) 6 of 45 Original Image y Enhanced Image x Image f (x, y) y s = intensitymax - r Image f (x, y) x
![Images taken from Gonzalez Woods Digital Image Processing 2002 7 of 45 Point Images taken from Gonzalez & Woods, Digital Image Processing (2002) 7 of 45 Point](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-7.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 7 of 45 Point Processing Example: Thresholding transformations are particularly useful for segmentation in which we want to isolate an object of interest from a background s = 1. 0 r > threshold 0. 0 r <= threshold
![Point Processing Example Thresholding cont 8 of 45 Original Image y Image f x Point Processing Example: Thresholding (cont…) 8 of 45 Original Image y Image f (x,](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-8.jpg)
Point Processing Example: Thresholding (cont…) 8 of 45 Original Image y Image f (x, y) s= Enhanced Image x 1. 0 0. 0 y Image f (x, y) r > threshold r <= threshold x
![Images taken from Gonzalez Woods Digital Image Processing 2002 9 of 45 Intensity Images taken from Gonzalez & Woods, Digital Image Processing (2002) 9 of 45 Intensity](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-9.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 9 of 45 Intensity Transformations
![Images taken from Gonzalez Woods Digital Image Processing 2002 10 of 45 Basic Images taken from Gonzalez & Woods, Digital Image Processing (2002) 10 of 45 Basic](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-10.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 10 of 45 Basic Grey Level Transformations There are many different kinds of grey level transformations Three of the most common are shown here – Linear • Negative/Identity – Logarithmic • Log/Inverse log – Power law • nth power/nth root
![11 of 45 Logarithmic Transformations The general form of the log transformation is s 11 of 45 Logarithmic Transformations The general form of the log transformation is s](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-11.jpg)
11 of 45 Logarithmic Transformations The general form of the log transformation is s = c * log(1 + r) 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
![Images taken from Gonzalez Woods Digital Image Processing 2002 12 of 45 Logarithmic Images taken from Gonzalez & Woods, Digital Image Processing (2002) 12 of 45 Logarithmic](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-12.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 12 of 45 Logarithmic Transformations (cont…) Log functions are particularly useful when the input grey level values may have an extremely large range of values In the following example the Fourier transform of an image is put through a log transform to reveal more detail s = log(1 + r)
![13 of 45 Logarithmic Transformations cont Original Image y Enhanced Image x Image f 13 of 45 Logarithmic Transformations (cont…) Original Image y Enhanced Image x Image f](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-13.jpg)
13 of 45 Logarithmic Transformations (cont…) Original Image y Enhanced Image x Image f (x, y) y x Image f (x, y) s = log(1 + r) We usually set c to 1 Grey levels must be in the range [0. 0, 1. 0]
![Images taken from Gonzalez Woods Digital Image Processing 2002 14 of 45 Power Images taken from Gonzalez & Woods, Digital Image Processing (2002) 14 of 45 Power](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-14.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 14 of 45 Power Law Transformations Power law transformations have the following form s=c*rγ 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
![15 of 45 Power Law Transformations cont Original Image y Enhanced Image x y 15 of 45 Power Law Transformations (cont…) Original Image y Enhanced Image x y](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-15.jpg)
15 of 45 Power Law Transformations (cont…) Original Image y Enhanced Image x y Image f (x, y) x Image f (x, y) s=rγ We usually set c to 1 Grey levels must be in the range [0. 0, 1. 0]
![16 of 45 Power Law Example 16 of 45 Power Law Example](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-16.jpg)
16 of 45 Power Law Example
![17 of 45 Power Law Example cont γ 0 6 17 of 45 Power Law Example (cont…) γ = 0. 6](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-17.jpg)
17 of 45 Power Law Example (cont…) γ = 0. 6
![18 of 45 Power Law Example cont γ 0 4 18 of 45 Power Law Example (cont…) γ = 0. 4](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-18.jpg)
18 of 45 Power Law Example (cont…) γ = 0. 4
![19 of 45 Power Law Example cont γ 0 3 19 of 45 Power Law Example (cont…) γ = 0. 3](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-19.jpg)
19 of 45 Power Law Example (cont…) γ = 0. 3
![Power Law Example cont The images to the right show a magnetic resonance MR Power Law Example (cont…) The images to the right show a magnetic resonance (MR)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-20.jpg)
Power Law Example (cont…) The images to the right show a magnetic resonance (MR) image of a fractured human spine Different curves highlight different detail s = r 0. 6 s = r 0. 4 Images taken from Gonzalez & Woods, Digital Image Processing (2002) 20 of 45 s= r 0. 3
![21 of 45 Power Law Example 21 of 45 Power Law Example](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-21.jpg)
21 of 45 Power Law Example
![22 of 45 Power Law Example cont γ 5 0 22 of 45 Power Law Example (cont…) γ = 5. 0](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-22.jpg)
22 of 45 Power Law Example (cont…) γ = 5. 0
![Power Law Transformations cont An aerial photo of a runway is shown This time Power Law Transformations (cont…) An aerial photo of a runway is shown This time](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-23.jpg)
Power Law Transformations (cont…) An aerial photo of a runway is shown This time power law transforms are used to darken the image Different curves highlight different detail s = r 3. 0 s = r 4. 0 Images taken from Gonzalez & Woods, Digital Image Processing (2002) 23 of 45 s= r 5. 0
![Images taken from Gonzalez Woods Digital Image Processing 2002 24 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 24 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-24.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 24 of 45 Gamma Correction • Different camera sensors - Have different responses to light intensity - Produce different electrical signals for same input • How do we ensure there is consistency in: a)Images recorded by different cameras for given light input b)Light emitted by different display devices for same image?
![Images taken from Gonzalez Woods Digital Image Processing 2002 25 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 25 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-25.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 25 of 45 Gamma Correction • What is the relation between: Camera: Light on sensor vs. “intensity” of corre sponding pixel Display: Pixel intensity vs. light from that pixel • Relation between pixel value and corre sponding physical quantity is usually complex, nonlinear
![Images taken from Gonzalez Woods Digital Image Processing 2002 26 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 26 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-26.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 26 of 45 Gamma Correction Many of you might be familiar with gamma correction of computer monitors Problem is that display devices do not respond linearly to different intensities Can be corrected using a log transform
![Images taken from Gonzalez Woods Digital Image Processing 2002 27 of 45 Gamma Images taken from Gonzalez & Woods, Digital Image Processing (2002) 27 of 45 Gamma](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-27.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 27 of 45 Gamma Correction
![Images taken from Gonzalez Woods Digital Image Processing 2002 28 of 45 More Images taken from Gonzalez & Woods, Digital Image Processing (2002) 28 of 45 More](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-28.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 28 of 45 More Contrast Issues
![Images taken from Gonzalez Woods Digital Image Processing 2002 29 of 45 Piecewise Images taken from Gonzalez & Woods, Digital Image Processing (2002) 29 of 45 Piecewise](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-29.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 29 of 45 Piecewise Linear Transformation Functions Rather than using a well defined mathematical function we can use arbitrary user-defined transforms The images below show a contrast stretching linear transform to add contrast to a poor quality image
![Images taken from Gonzalez Woods Digital Image Processing 2002 30 of 45 Gray Images taken from Gonzalez & Woods, Digital Image Processing (2002) 30 of 45 Gray](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-30.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 30 of 45 Gray Level Slicing Highlights a specific range of grey levels – Similar to thresholding – Other levels can be suppressed or maintained – Useful for highlighting features in an image
![Images taken from Gonzalez Woods Digital Image Processing 2002 31 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 31 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-31.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 31 of 45 Bit Plane Slicing Often 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
![Images taken from Gonzalez Woods Digital Image Processing 2002 32 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 32 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-32.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 32 of 45 Bit Plane Slicing (cont…) [10000000] [01000000] [00100000] [00001000] [00000100] [00000001]
![33 of 45 Bit Plane Slicing cont 33 of 45 Bit Plane Slicing (cont…)](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-33.jpg)
33 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 34 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 34 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-34.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 34 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 35 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 35 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-35.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 35 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 36 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 36 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-36.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 36 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 37 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 37 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-37.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 37 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 38 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 38 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-38.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 38 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 39 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 39 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-39.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 39 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 40 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 40 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-40.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 40 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 41 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 41 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-41.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 41 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 42 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 42 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-42.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 42 of 45 Bit Plane Slicing (cont…)
![Images taken from Gonzalez Woods Digital Image Processing 2002 43 of 45 Bit Images taken from Gonzalez & Woods, Digital Image Processing (2002) 43 of 45 Bit](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-43.jpg)
Images taken from Gonzalez & Woods, Digital Image Processing (2002) 43 of 45 Bit Plane Slicing (cont…) Reconstructed image using only bit planes 8 and 7 Reconstructed image using only bit planes 8, 7 and 6 Reconstructed image using only bit planes 7, 6 and 5
![44 of 45 Report Histogram Specification 44 of 45 Report Histogram Specification](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-44.jpg)
44 of 45 Report Histogram Specification
![45 of 45 Summary We have looked at different kinds of point processing image 45 of 45 Summary We have looked at different kinds of point processing image](https://slidetodoc.com/presentation_image/441f78ac4c1a1b638bcf6e0cdbb54597/image-45.jpg)
45 of 45 Summary We have looked at different kinds of point processing image enhancement Next time we will start to look at neighbourhood operations – in particular filtering and convolution
Fractal image
پردازش تصویر
Histogram processing in digital image processing
Point processing operations
Nonlinear image processing
Morphological processing in digital image processing
Image transform in digital image processing
Noise
Image compression in digital image processing
Key stage in digital image processing
Fidelity criteria in digital image processing
Image sharpening and restoration
Image geometry in digital image processing
Zooming and shrinking in digital image processing
Image transform in digital image processing
Maketform matlab
Image restoration in digital image processing
Digital foil enhancement
Logarithmic transformation in image processing
Image enhancement in night vision technology
Objective of image enhancement
Contoh pelembutan citra
Spatial filtering
Image enhancement in spatial domain
Image enhancement in spatial domain
Image enhancement in spatial domain
Image enhancement
Image representation and description
Representation and description in digital image processing
Double thresholding matlab
Introduction to digital image processing
Explain basic relationship between pixels
Intensity transformation
Zooming and shrinking of digital images
Imadjust
8 adjacency in image processing
Coordinate conventions in digital image processing
Dam construction in digital image processing
Digital image processing java
Thresholding in digital image processing
Filteration
In digital image processing
What is boundary descriptors in digital image processing
Thresholding in digital image processing
Nd dot
Color slicing in image processing