Mathew Hong, Quinn Lewis, Udit Patidar

1. Enhancing the Enhancement: Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution Mathew Hong, Quinn Lewis, Udit Patidar

2. Overview • Problem Statement • Methods • Result • Conclusion

3. Problem Statement • Dealing with enhancement of grayscale images • Usually requires human involvement as an evaluator • So it is difficult to enhance images automatically

4. Problem Statement • Use Evolutionary Algorithms to search for best enhancement • Searches for the best configuration of parameters • Advantage: Automation of image enhancement process • Advantage: Takes use of suitable global search heuristics

5. Methods • Problem Statement • Methods • Result • Conclusion

6. Methods - Overview • Input - Image • Transformation and image processing part • Evolutionary components • Output - Enhanced Image • Testing part

7. Apply to each pixel • First normalise image input and use real global mean • Use neighborhood (window) to calculate both and • Find the best values for Enhancement Kernel

8. Enhancement Evaluation Criterion Establish a criterion for determining a good image: • High number of edges • Pixels belonging to an edge • Higher intensity of the edges • As compared to original image • “Entropic measure” • Based on histogram • Quantify number of gray–levels in an image

9. Enhancement Evaluation Criterion Details • Calculate “entropic measure” • Use Sobel edge detector to identify edges • Sum up intensities of edges • Count pixels greater than threshold (edgels) • Generate threshold automatically using estimation of the signal-to-noise ratio • The best enhancement maximizes the “entropic measure”, number of edgels, and sharp edges

10. Enhancement Evaluation Examples Relativity Good Image H = 7.1224 n = 4368200 E = 13382 Relativity Poor Image H = 6.8536 n = 3011500 E = 8303

11. Genetic Algorithm Background

12. Survival of the Fittest

13. Selection • Binary Tournament • Constant high selection pressure • Most fit of two randomly selected individuals becomes a parent. • K-elitist scheme • Assures the preservation of the K most fit individuals • Selection methods chosen to maximise exploitation.

14. Crossover • Arithmetic Crossover • Offspring genes close to parents’ genes • Focused and exploitative search

15. Mutation • Principle Component Analysis Mutation Figures from Cristian Munteanu Doctoral Dissertation

17. PCA Mutation • Explorative • Ensures diverse population • Prevents genetic drift and premature convergence • Computationally expensive if the chromosome is large

18. Objective Evaluation • Need some way to quantitatively describe the achievements of EVOLEHA • Since we have enhanced contrast, an objective evaluation criterion based on intensity was used

19. Detail Variance - Background Variance • Around every pixel, take variance of image intensities • Classify pixel into foreground or background based on a threshold • Average of variance of foreground pixels gives Detail Variance (DV) • Average of variance of background pixels gives Background Variance (BV)

20. DV/BV and image quality • Ideally, high DV and low BV characterise a good image • Techniques such as histogram equalisation and contrast stretching give higher DV than original image • In EVOLEHA, DV increases and BV stays almost the same • So, quantitatively, the image has been enhanced!

21. Results • Problem Statement • Methods • Results • Conclusion

22. Result

23. Results (a,b,c,kappa)=(0.48, 0.46, 0.46, 0.73) Time = 1.90 e+003 (a,b,c,kappa)=(1.21, 1.25, 0.44, 0.44) Time = 3.04 e+004

24. Results of evaluation • We expect increase in DV values • Image used - boat64.raw • a=0.7, b=0.2, c=0.7, kappa=0.75, t=0.005 • OriginaI, DV = 0.0154 BV = 0.0027 • Hist equalised DV = 0.0186 BV = 0.0031 • Contrast stretch DV = 0.0154 BV = 0.0027 • EVOLEHAised, DV = 0.0207 BV = 0.0050

26. Conclusion • Problem Statement • Methods • Results • Conclusion

27. Conclusion • A powerful grayscale image enhancement technique whichleads to high contrast enhancement • SLOW • Need to reduce population size and/or maximum number of required generations • EVOLEHA outperforms other automatic methods but with great computational cost

28. Acknowledgements • Cristian Munteanu • Dr. Shah

29. References • C. Munteanu and V. Lazarescu, “Improving mutation capabilities in a real-coded GA,” in Proc. Of EvoIASP. Berlin, Germany: Springer-verlag, 1999, pp. 138-149 • C. Munteanu and A. Rosa, “Gray-scale Image Enhancement as an Automatic Process Driven by Evolution,” in Systems, Man and Cybernetics, Part B, IEEE Transactions on, Volume: 34, Issue: 2, April 2004, pp. 1292-1298 • C. Munteanu, “Doctoral Dissertation: Chapter 5.1 Pricipal Component Analysis (PCA) Mutation: Motivations and Theoretical Aspects”, pp.120-127 • T. Back and F. Hoffmeister, “Extended Selection Mechanisms in Genetic Algorithms,” in Proceedings of the Fourth International Conference on Genetic Algorithms and their Application, San Mateo, California, USA: Morgan Kaufmann Publishers, 1991, pp. 92-99 • G. Ramponi, N. Strobel, S. K. Mitra, and T.-H. Yu, “Nonlinear unsharp masking methods for image contrast enhancement,” J. Electron. Imaging, vol. 5, no. 3, pp. 353-366, 1996.