3. Point Processing (Szeliski 3.1) cs129: Computational Photography James Hays, Brown, Fall 2012 Some figures from Alexei Efros, Steve Seitz, and Gonzalez et al.

4. Image Processing image filtering: change range of image g(x) = h(f(x)) f x h f x f x h f x image warping: change domain of image g(x) = f(h(x))

5. Image Processing hh f f g g image filtering: change range of image g(x) = h(f(x)) image warping: change domain of image g(x) = f(h(x))

6. Point Processing The simplest kind of range transformations are these independent of position x,y: g = t(f) This is called point processing. What can they do? What’s the form of t? Important: every pixel for himself – spatial information completely lost!

7. Negative

8. Basic Point Processing

9. Log

10. For example: Gamma “correction” Is an instance of these power law intensity transformations. Typically, gamma = 2.2 for a display device, and 1/2.2 for encoding. The result is a more perceptually uniform encoding.

11. Gamma “correction”

12. Contrast Stretching

13. Image Histograms Cumulative Histograms s = T(r)

14. Histogram Equalization

15. Limitations of Point Processing Q: What happens if I reshuffle all pixels within the image? A: It’s histogram won’t change. No point processing will be affected…