dipslideshare.ppsx
Download as PPSX, PDF0 likes7 views
Histogram equalization is a method for adjusting image intensities to enhance contrast by equalizing the histogram of the input image. It transforms the intensities so that the histogram of the output image approximately matches a uniform distribution. The technique maps the intensity levels in the input image to a new range of values based on the probability distribution function of the original histogram to flatten and widen it, resulting in an increased contrast in the output image.
![Histogram Processing
• Intensity levels in the range [0, L-1]
• Discrete function h(rk)=nk
• Histogram Normalization
• p(rk)=nk/MN
• Horizontal axis – intensity values rk
• Vertical axis - h(rk)=nk
• or p(rk)=nk/MN
• Four Basic image types Histogram](https://image.slidesharecdn.com/dipslideshare-220622022301-4265a9c6/85/dipslideshare-ppsx-2-320.jpg)
dipslideshare.ppsx
- 1. Intensity Transformation and Spatial Filtering 25.08.2020 11:30 am
- 2. Histogram Processing • Intensity levels in the range [0, L-1] • Discrete function h(rk)=nk • Histogram Normalization • p(rk)=nk/MN • Horizontal axis – intensity values rk • Vertical axis - h(rk)=nk • or p(rk)=nk/MN • Four Basic image types Histogram
- 3. Histogram Equalization • T(r) is monotonically increasing function • 0 ≤ T(r) ≤ L – 1 • Using Inverse formulation r=T-1(s) • T(r) is strictly monotonically increasing function