1. 09 March 1999 Digital Image Processing II 1. Single-Band Image Processing Histogram Image contrast enhancement (Linear stretch, histogram equalization) Spatial feature enhancement Edge enhancement Filters 2. Multiple-Band Image Processing Colour display basics

2. 09 March 1999 Histogram Histogram is a diagram showing the distribution of the number of pixels, or frequency, with digital number in an image. Lillesand p 544-545

3. 09 March 1999 Contrast manipulation Lillesand-Kiefer 7.4 Histogram No Stretch Linear Stretch Histogram Stretch Special Stretch

4. 09 March 1999 Linear Histogram Stretch 0 255 0 DN (original image) DN (enhanced image) DN min DN max

5. 09 March 1999 Linear Histogram Stretch 1. Min/Max Contract Stretch Screen DN = --------------------------- X 255 pixel DN – DN min DN max -DN min 2. Linear Stretch Based on Standard Deviations DN max and DN min are the maximum and minimum DN values in an image DN min = mean DN – (2 x standard deviation of all image DN values) DN max = mean DN + (2 x standard deviation of all image DN values) For 8 bit images

6. 09 March 1999 Histogram Equalization The goal is to “flatten out” the histogram such that there is an even distribution of of number of pixels assigned to each video intensity DN Number of pixels DNmin DNmax DN Number of pixels DNmin DNmax For an example, see Ch. 2, Verbyla

7. 09 March 1999 Spatial Feature Enhancement 1. Spatial filtering Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency refers to the frequency of the variations in tone that appear in an image. CCRS WWW Lillesand-Kiefer 7.5 Image areas of -high spatial frequency are tonally “rough” -low spatial frequency are tonally “smooth” Spatial filtering is one application of image processing operations CONVOLUTION

8. 09 March 1999 Spatial Feature Enhancement 2. Edge Enhancement Edge enhancement attempts to preserve both local contrast and low frequency brightness information 1. Production of a high frequency component image. 2. All, or a fraction, of the grey level in each pixel in the original image is added back to the high frequency component image. 3. The composite image is contrast stretched. Lillesand-Kiefer 7.5 Edge enhancement is usually implemented in 3 steps:

9. 09 March 1999 Edge Enhancement Directional first differencing AH V D Horizontal first difference = DN A - DN H Lillesand-Kiefer 7.5 Vertical first difference = DN A - DN V Diagonal first difference = DN A - DN D Usually, a constant value ( 127 for 8-bit) is added to the difference

10. 09 March 1999 Edge Enhancement Numerical example of Directional first differencing Vertical first difference = DN A - DN V 23 5 7 2-5+127= 124 3-7+127= 123

11. 09 March 1999 Example of directional first differencing Original image Horizontal first difference Lillesand-Kiefer 7.5

12. 09 March 1999 Example of directional first differencing Diagonal first differenceVertical first difference Lillesand-Kiefer 7.5

13. 09 March 1999 Example of cross-diagonal first differencing This enhancement tends to highlight all edges in the scene Lillesand-Kiefer 7.5

14. 09 March 1999 Spatial Feature Enhancement 3. Fourier Analysis Lillesand-Kiefer 7.5 This is a powerful method to enhance spatial features in an image. The actual calculations are done in the spatial frequency domain. (not the same as the “time frequency” usually used in wavelength-frequency conversion) Original Image Fourier spectrum of the image