This study is based on improving the visual quality of the images captured under different illumination conditions, i.e. overexposed and underexposed. This study presents a fuzzy image enhancement process based on a finite fuzzy set which is defined to optimise entropy, noise, intensity and edge information. In the proposed fuzzy approach, the overexposed and underexposed images are mapped to form a fuzzy set using membership functions. In the fuzzification process, the degree of belonging of each input to an appropriate fuzzy membership is calculated with respect to intensity, entropy, edge information and background noise. Therefore, the proposed method preserves the details of the image. Indeed, the fuzzy systems are well suited to model the uncertainty that occurs when conflicting operations are performed. Some effective approaches can enhance the image data without increasing noise. However, their ability to reduce the noise during the sharpening process is limited. The proposed method enhances the image by controlling sharpness parameter which affects the visual quality of the image. Both qualitative and quantitative assessments are performed to evaluate the performance of the proposed algorithm.

References

1. 1)
  - 45. Perfilieva, I.: ‘Fuzzy transforms, transactions on rough sets II. Fuzzy sets and rough sets’ (Springer-Verlag, Berlin, 2004), pp. 63–81, ISBN 3-540-23990-1.
2. 2)
  - 15. Perfilieva, I., Dankov, M.: ‘Image fusion on the basis of fuzzy transforms’. World Scientific Proc. Series on Computer Engineering and Information Science, Computational Intelligence in Decision and Control, January 2008, vol. 1, pp. 471–476.
3. 3)
  - 40. Rahman, S.M.M., Ahmad, M.O., Swamy, M.N.S.: ‘Contrast-based fusion of noisy images using discrete wavelet transform’, IET Image Process., 2010, 4, (5), pp. 374–384.
4. 4)
  - 43. Kannan, K., Perumal, S.A., Arulmozhi, K.: ‘Performance comparison of various levels of fusion of multi-focused images using wavelet transform’, Int. J. Comput. Applic., 2010, 1, (6), pp. 71–78.
5. 5)
  - 20. Höppner, F., Klawonn, F., Kruse, R., et al: ‘Fuzzy cluster analysis: methods for classification, data analysis and image recognition’ (John Wiely & Sons, Chichester, 1999).
6. 6)
  - 33. Choi, M.: ‘A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter’, IEEE Trans. Geosci. Remote Sens., 2006, 44, (6), pp. 1672–1682.
7. 7)
  - 27. An, G., Wu, J., Ruan, Q.: ‘Independent Gabor analysis of multiscale total variation-based quotient image’, IEEE Signal Process. Lett., 2008, 15, pp. 186–189.
8. 8)
  - 38. Chen, Y., Xue, Z.Y., Blum, R.S.: ‘Theoretical analysis of an information-based quality measure for image fusion’, Inf. Fusion, 2008, 9, (2), pp. 161–175.
9. 9)
  - 32. Available at http://imagefusion.org.
10. 10)
  - 39. Desale, R.P., Verma, S.V.: ‘Study and analysis of PCA, DCT & DWT based image fusion techniques’. Int. Conf. on Signal Processing, Image Processing and Pattern Recognition, 7–8 February 2013, pp. 66–69.
11. 11)
  - 41. Arivazhagan, S., Ganesan, L., Kumar, T.G.S.: ‘A modified statistical approach for image fusion using wavelet transform’, Signal Image Video Process., 2009, 3, (2), pp. 137–144.
12. 12)
  - 17. Zimmermann, H.J.: ‘Fuzzy set theory and its applications’ (Kluwer Academic Publishers, 1991, 2nd edn.).
13. 13)
  - 44. Ahmadvand, A., Daliri, M.R.: ‘Invariant texture classification using a spatial filter bank in multi-resolution analysis’, Image Vis. Comput., 2016, 45, pp. 1–10.
14. 14)
  - 7. Yang, C., Zhang, J.Q., Wang, X.R., et al: ‘A novel similarity based quality metric for image fusion’, Inf. Fusion, 2008, 9, (2), pp. 156–160.
15. 15)
  - 30. Horn, B.K.P.: ‘Robot vision’ (MIT Press, Cambridge, MA, 1997).
16. 16)
  - 31. Goldfarb, D., Yin, W.: ‘Parametric maximum flow algorithms for fast total variation minimization’, SIAM J. Sci. Comput., 2009, 31, (5), pp. 3712–3743.
17. 17)
  - 28. Du, S., Ward, R.: ‘Wavelet-based illumination normalization for face recognition’. IEEE Int. Conf. on Image Processing, Genova, Italy, September 2005.
18. 18)
  - 29. Basri, R., Jacobs, D.: ‘Lambertian reflectance and linear subspaces’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (2), pp. 218–233.
19. 19)
  - 8. Vu, C.T., Phan, T.D., Chandler, D.M.: ‘S3: a spectral and spatial measure of local perceived sharpness in natural images’, IEEE Trans. Image Process., 2012, 21, (3), pp. 934–945.
20. 20)
  - 4. Yang, H., Shao, L., Zheng, F., et al: ‘Recent advances and trends in visual tracking: a review’, Neurocomputing, 2011, 74, (18), pp. 3823–3831.
21. 21)
  - 6. Yang, B., Li, S.: ‘Multifocus image fusion and restoration with sparse representation’, IEEE Trans. Instrum. Meas., 2010, 59, (4), pp. 884–892.
22. 22)
  - 24. Barrenechea, E., Bustince, H., Beats, D.B., et al: ‘Construction of interval-valued fuzzy relations with application to the generation of fuzzy edge images’, IEEE Trans. Fuzzy Syst., 2011, 19, (5), pp. 819–830.
23. 23)
  - 46. Dvorak, A., Habiballa, H., Novak, V., et al: ‘The concept of LFLC 2000—its specificity, realization and power of applications’, Comput. Ind., 2003, 51, pp. 269–280.
24. 24)
  - 22. Jian-Lei, L., Jing-Xiu, Z., Bo, L.: ‘A self-adaptable method of edge detection based on the gradient magnitude’, Optoelectron. Technol., 2007, 27, (3), pp. 174–177.
25. 25)
  - 25. Chen, S.M., Kao, P.Y.: ‘TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines’, Inf. Sci., 2013, 247, pp. 62–71.
26. 26)
  - 21. Viattchenin, D.A.: ‘A heuristic approach to possibilistic clustering: algorithms and applications’ (Springer-Verlag, Berlin, 2013).
27. 27)
  - 10. Yang, Y.: ‘A novel DWT based multi-focus image fusion method’, Procedia Eng., 2011, 24, pp. 117–181.
28. 28)
  - 23. Zhi, W., Sai-Xian, H.: ‘An adaptive edge-detection method based on canny algorithm’, J. Image Graph., 2004, 9, (8), pp. 957–962.
29. 29)
  - 5. Wang, Q., Fang, J., Yuan, Y.: ‘Multi-cue based tracking’, Neurocomputing, 2014, 131, (1), pp. 227–236.
30. 30)
  - 12. Cheng, H.D., Shi, X.J.: ‘A simple and effective histogram equalization approach to image enhancement’, Digit. Signal Process., 2004, 14, (2), pp. 158–170.
31. 31)
  - 13. Huang, S.C., Cheng, F.C., Chiu, Y.S.: ‘Efficient contrast enhancement using adaptive gamma correction with weighting distribution’, IEEE Trans. Image Process., 2013, 22, (3), pp. 1032–1041.
32. 32)
  - 34. Zheng, H., Zheng, D., Hu, Y., et al: ‘Study on the optimal parameters of image fusion based on wavelet transform’, J. Comput. Inf. Syst., 2010, 6, (1), pp. 131–137.
33. 33)
  - 26. Roubos, H., Setnes, M.: ‘Compact and transparent fuzzy models and classifiers through iterative complexity reduction’, IEEE Trans. Fuzzy Syst., 2001, 9, (4), pp. 516–524.
34. 34)
  - 19. Bezdek, J.C.: ‘Pattern recognition with fuzzy objective function algorithms’ (Springer, 1981).
35. 35)
  - 3. Yuan, Y., Fang, J., Wang, Q.: ‘Robust super pixel tracking via depth fusion’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (1), pp. 15–26.
36. 36)
  - 16. Dhingra, N., Nandal, A., Manchanda, M., et al: ‘Fusion of fuzzy enhanced overexposed and underexposed images’, Procedia Comput. Sci., 2015, 54, pp. 738–745.
37. 37)
  - 36. Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: ‘A non-reference image fusion metric based on mutual information of image features’, Comput. Electr. Eng., 2011, 37, (5), pp. 744–756.
38. 38)
  - 18. Klir, J.G., Yuan, B.: ‘Fuzzy set and fuzzy logic: theory and applications’ (Prentice Hall, 1995).
39. 39)
  - 9. Petrović, V.: ‘Subjective tests for image fusion evaluation and objective metric validation’, Inf. Fusion, 2007, 8, (2), pp. 208–216.
40. 40)
  - 42. Pajares, G., Cruz, J.M.: ‘A wavelet-based image fusion tutorial’, J. Pattern Recognit., 2004, 37, (8), pp. 1855–1872.
41. 41)
  - 1. Goshtasby, A.A., Nikolov, S.: ‘Advances in the state of art’, Inf. Fusion, 2007, 8, (2), pp. 114–118.
42. 42)
  - 2. Yang, B., Jing, Z., Zhao, H.: ‘Review of pixel level image fusion’, J. Shanghai Jiaotong Univ. (Sci.), 2010, 15, (1), pp. 6–12.
43. 43)
  - 35. Perez-Cruz, F., Navia-Vazquez, F., Figueiras-Vidal, A.R., et al: ‘Empirical risk minimization for support vector classifiers’, IEEE Trans. Neural Netw., 2003, 14, (2), pp. 296–303.
44. 44)
  - 11. Suman, S., Hussin, F.A., Malik, A.S., et al: ‘Image enhancement using geometric mean filter and gamma correction for WCE images’. Neural Information Processing, November 2014 (LNCS, 8836), pp. 276–283.
45. 45)
  - 14. Nandal, A., Rosales, H.G.: ‘Enhanced image fusion using directional contrast rules in fuzzy transform domain’, SpringerPlus, 2016, 5, (1), p. 1846.
46. 46)
  - 37. Wadud, M. A. A., Kabir, M. H., Dewan, M. A. A., et al: ‘A dynamic histogram equalization for image contrast enhancement’, IEEE Trans. Consum. Electron., 2007, 53, (2), pp. 593–600.

Fuzzy enhanced image fusion using pixel intensity control

References

Related content