“Image enhancement by unsharp masking the depth buffer” by Luft, Colditz and Deussen
Conference:
Type(s):
Title:
- Image enhancement by unsharp masking the depth buffer
Presenter(s)/Author(s):
Abstract:
We present a simple and efficient method to enhance the perceptual quality of images that contain depth information. Similar to an unsharp mask, the difference between the original depth buffer content and a low-pass filtered copy is utilized to determine information about spatially important areas in a scene. Based on this information we locally enhance the contrast, color, and other parameters of the image. Our technique aims at improving the perception of complex scenes by introducing additional depth cues. The idea is motivated by artwork and findings in the field of neurology, and can be applied to images of any kind, ranging from complex landscape data and technical artifacts, to volume rendering, photograph, and video with depth information.
References:
1. Adams, A. 1980. The Camera. The Ansel Adams Photography Series. Littel, Brown and Company.Google Scholar
2. Beghdadi, A., and le Negrate, A. 1989. Contrast enhancement technique based on local detection of edges. Computer Vision, Graphics, and Image Processing 46, 2, 162–174. Google ScholarDigital Library
3. Bunnell, M. 2005. Dynamic ambient occlusion and indirect lighting. In GPU Gems 2. Addison-Wesley, 223–233.Google Scholar
4. Cignoni, P., Scopigno, R., and Tarini, M. 2005. A simple normal enhancement technique for interactive non-photorealistic renderings. Computer & Graphics 29, 1 (feb), 125–133. Google ScholarDigital Library
5. Deussen, O., and Strothotte, T. 2000. Computer-generated pen-and-ink illustration of trees. In Proceedings of ACM SIGGRAPH 2000, 13–18. Google ScholarDigital Library
6. DiCarlo, J. M., and Wandell, B. A. 2000. Rendering high dynamic range images. In Proceedings of SPIE: Image Sensors, vol. 3965, 392–401.Google Scholar
7. Eysenck, M., and Keane, M. 2000. Cognitive Psychology. Psychology Press.Google Scholar
8. Gooch, A., Gooch, B., Shirley, P., and Cohen, E. 1998. A non-photorealistic lighting model for automatic technical illustration. In Proceedings of ACM SIGGRAPH 98, 447–452. Google ScholarDigital Library
9. Hummel, R. A. 1975. Histogram modification techniques. Computer Graphics and Image Processing 4, 3 (sep), 209–224.Google ScholarCross Ref
10. Ledda, P., Chalmers, A., Troscianko, T., and Seetzen, H. 2005. Evaluation of tone mapping operators using a high dynamic range display. ACM Transactions on Graphics 24, 3 (aug), 640–648. Google ScholarDigital Library
11. McHugh, S., 2005. Digital photography tutorials. http://www.cambridgeincolour.com/tutorials.htm.Google Scholar
12. Meylan, L., and Süsstrunk, S. 2004. Bio-inspired color image enhancement. In Proceedings of SPIE: Human Vision and Electronic Imaging, vol. 5292, 46–56.Google Scholar
13. Neycenssac, F. 1993. Contrast enhancement using the laplacian-of-a-gaussian filter. CVGIP: Graphical Models and Image Processing 55, 6, 447–463. Google ScholarDigital Library
14. Pharr, M., and Green, S. 2004. Ambient occlusion. In GPU Gems. Addison-Wesley, 279–292.Google Scholar
15. Raskar, R., Tan, K.-H., Feris, R., Yu, J., and Turk, M. 2004. Non-photorealistic camera: Depth edge detection and stylized rendering using multi-flash imaging. ACM Transactions on Graphics 23, 3, 679–688. Google ScholarDigital Library
16. Reinhard, E., and Devlin, K. 2005. Dynamic range reduction inspired by photoreceptor physiology. IEEE Transactions on Visualization and Computer Graphics 11, 1 (jan), 13–24. Google ScholarDigital Library
17. Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. 2002. Photographic tone reproduction for digital images. ACM Transactions on Graphics 21, 3 (jul), 267–276. Google ScholarDigital Library
18. Saito, T., and Takahashi, T. 1990. Comprehensive rendering of 3-d shapes. Computer Graphics (Proceedings of ACM SIGGRAPH 90) 24, 4, 197–206. Google ScholarDigital Library
19. Scharstein, D., and Szeliski, R. 2003. High-accuracy stereo depth maps using structured light. In Proceedings of Computer Vision and Pattern Recognition, 195–202. Google ScholarDigital Library
20. Starck, J., Murtagh, F., Candes, E., and Donoho, D. 2003. Gray and color image contrast enhancement by the curvelet transform. IEEE Transactions on Image Processing 12, 6, 706–717. Google ScholarDigital Library
21. Stark, J. 2000. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing 9, 5 (may), 889–896. Google ScholarDigital Library
22. Winkenbach, G., and Salesin, D. 1994. Computer-generated pen-and-ink illustration. In Proceedings of ACM SIGGRAPH 94, 91–100. Google ScholarDigital Library
23. Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S., and Szeliski, R. 2004. High-quality video view interpolation using a layered representation. ACM Transactions on Graphics 23, 3 (aug), 600–608. Google ScholarDigital Library