1. Contrast-Enhanced Black and White Images Hua Li and David Mould UNC Wilmington and Carleton University Presented by Ling Xu lihua@uncw.edu1

2. The Problem of Tone Reduction It is difficult to obtain a clear segmentation without user intervention. @Flickr user Bernard Levine lihua@uncw.edu2

3. Related Work Thresholding methods lihua@uncw.edu3 Details are missing. Region boundaries have isolated pixels. Our method preserves the details very well.

4. Related Work in Non-Photorealistic Rendering (NPR) --- Region-Based Methods [XKM07, MG08, XK08, RL10, MZZ10] lihua@uncw.edu4 By Xu and Kaplan [XK08] Our method Highly rely on the results from segmentation methods

5. Related Work in Non-Photorealistic Rendering (NPR) --- Filter-Based Methods [Win11,WKO12] lihua@uncw.edu5 Our method By Winnemöller et. al [Win11,WKO12] Sharp corners are generally missing, and unwanted edge extensions may depict the content wrongly.

6. Our Algorithm Inspired by Stick filtering Several stages: 1.initial global blurring 2.a conversion from a color image to an enhanced and smoothed greyscale image 3.binary thresholding 4.final cleanup -- further smoothing and removal of small clusters lihua@uncw.edu6

7. Step 2: a conversion from a color image to an enhanced and smoothed greyscale image  For each pixel, running statistical analysis on a few approximated directions  Contrast is increased along the direction of maximum response, amplifying local tonal differences.  Or smoothing is applied when the pixel is in an area with little texture or few edges.  This conversion is iterated for a couple of times. lihua@uncw.edu7

8. Our Algorithm lihua@uncw.edu8 Original image Contrast-enhanced without smoothing Contrast-enhanced and smoothed Final effect after thresholding and removal of small regions

9. Core Ideas about Contrast lihua@uncw.edu9 amplifying local tonal differences iteratively Original

10. Statistical Analysis on each pixel For each pixel, we approximate n directions and study the statistical values to decide if a pixel needs to be darkened or lightened. lihua@uncw.edu10 The direction of maximum difference between the average intensity of a stick and the average intensity of the local area. Chosen

11. lihua@uncw.edu11

12. Noise Suppression Using Global and Local Smoothness lihua@uncw.edu12 Original image Contrast-enhanced without smoothness Contrast-enhanced and smoothed

13. Variations of Our Algorithm lihua@uncw.edu13

14. Variations of Our Algorithm lihua@uncw.edu14

15. Variations of Our Algorithm lihua@uncw.edu15

16. Our Method lihua@uncw.edu16 Xu and Kaplan’s Method

17. Comparisons lihua@uncw.edu17

18. Comparisons lihua@uncw.edu18

19. lihua@uncw.edu19

20. lihua@uncw.edu 20 Our method By Winnemöller et. al [Win11,WKO12]

21. lihua@uncw.edu21

22. Advantages o Automatic o Simple implementation o High quality in structural preservation o Control over the detail and the abstraction lihua@uncw.edu22

23. Limitations o Demands multiple iterations slower than the XDOG method o Demands the configuration of a set of parameters the same as previous methods o Demands further investigation on the variations lihua@uncw.edu23

24. More Results lihua@uncw.edu24

25. Acknowledgements Thanks to Ling Xu for the presentation. Thanks to Holger Winnemöller for sharing the images. Thanks to the reviewers for the comments. Thanks to Peter Selinger for the Potrace tool. lihua@uncw.edu25

26. Questions? Please send your questions to Hua Li directly. LIHUA@UNCW.EDU lihua@uncw.edu26