A new image enhancement method considering both dynamic range and color constancy

Document Type : Article


1 Department of Computer Engineering, Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran.

2 Department of Computer Engineering, Kosar University of Bojnord, Bojnord, Iran.


This paper proposes an approach to improve color images suffering from low dynamic range, by employing both histogram matching and histogram stretching techniques. Firstly, the color image is transformed from RGB into HSV color space, in which the color information is separated from intensity. Then, an appropriate reference image is selected by comparing component V of the enhancing image with component V of the data base images using a similarity measure. This selected image is used as the goal image in histogram matching algorithm in order to enhance image brightness (Automatic Histogram Matching). Secondly, components V and S are linearly stretched in order to recover the image color information. Finally, using the treated V and S components and untreated component of H, the enhanced image is obtained in RGB color space by inverse transform. The qualitative and quantitative results show that the contrast and color of resultant images are greatly improved using the proposed method, which outperforms the current state-of-the-art methods.


Main Subjects

1.Su, F., Fang, G., and Kwok, N.M. Adaptive colour feature identi_cation in image for object tracking", Math. Probl. Eng., Article ID 509597, 2012, 18 pages (2012). http://dx.doi.org/10.1155/2012/509597
2. Hassanpour, H., Samadiani, N., and Mahdi Salehi, S.M. Using morphological transforms to enhance the contrast of medical images", Egyptian J. Radiology Nuclear, 46(2), pp. 481-489 (2015).
3. Blaschke, T., Hay, G.J., Kelly, M., et al. Geographic object-based image analysis- toward a new paradigm", ISPRS J. Photogramm, 87, pp. 180-191 (2014). 4. Zhuang, P., Fu, X., Huang, Y., and Ding, X. Image enhancement using divide-and-conquer strategy", J. Vis. Commun. Image. Represent, 45, pp. 137-146 (2017). 5. Wang, L., Xiao, L., Liu, H., and Wei, Z. Variational Bayesian method for Retinex", IEEE Trans. Image Process., 23, pp. 3381-3396 (2014). 6. Zhang, R., Feng, X., Yang, L., Chang, L., and Xu, C. Global sparse gradient guided variational Retinex model for image enhancement", Signal Process: Image Commun., 58, pp. 270-281 (2017). 7. Deng, G. A generalized unsharp masking algorithm", IEEE Trans. Image Process., 20, pp. 1249-1261 (2011). 8. Lin, S.C.F., Wong, C.Y., Jiang, G., et. al. Intensity and edge based adaptive unsharp masking _lter for color image enhancement", Int. J. for Light and Electron (Optics), 27, pp. 407-414 (2016). 9. Menotti, D., Najman, L., Facon, J., and Araujo, A. Multi-histogram equalization methods for contrast enhancement and brightness preserving", IEEE Trans Consum. Electr., 53, pp. 1186-1194 (2007). 10. Maini, R. and Aggarwal, H. A comprehensive review of image enhancement techniques", J. Comput., 2, pp. 8-13 (2010). 11. Celik, T. Two-dimensional histogram equalization and contrast enhancement", Comm. Com. Inf. Sc., 45, pp. 3810-3824 (2012). 12. Celik, T. Spatial entropy-based global and local image contrast enhancement", IEEE Trans. Image Process, 23, pp. 5298-5308 (2014). 13. Kwak, H.J. and Park, G.T. Image contrast enhancement for intelligent surveillance systems using multilocal histogram transformation", J. Intell. Manuf., 25, pp. 303-318 (2012). H. Hassanpour and N. Samadiani/Scientia Iranica, Transactions D: Computer Science & ... 26 (2019) 1589{1600 1599 14. Wang, X. and Chen, L. An e_ective histogram modi_- cation scheme for image contrast enhancement", Signal Process: Image Commun., 58, pp. 187-198 (2017). 15. Xiao, B., Jiang, H., Li, W., and Wang, G. Brightness and contrast controllable image enhancement based on histogram speci_cation", Neurocomputing, 275(c), pp. 2798-2809 (2018). 16. Chen, J., Yu, W., Tian, J., Chen, L., and Zhou, Z. Image contrast enhancement using an arti_cial bee colony algorithm", Swarm Evol Comput., 38, pp. 287- 294 (2018). 17. Hashemi, S., Kiani, S., Noroozi, N., and Ebrahimi, M. An image contrast enhancement method based on genetic algorithm", Pattern Recognit. Lett., 31(13), pp. 1816-1824 (2010). 18. Kim, S.E., Jeon, J.J., and Eom, I.K. Image contrast enhancement using entropy scaling in wavelet domain", Signal Process, 127, pp. 1-11 (2016). 19. Ehsani, S.P., Mousavi, H.S., and Khalaj, B.H. Iterative histogram matching algorithm for chromosome image enhancement based on statistical moments", IEEE Int. Sym. on Biomedical Imaging (ISBI), Barcelona, Spain, pp. 214-217 (2012). 20. Raji, R., Mishra, D., and Nair, M.S. A novel texture based automated histogram speci_cation for colour image enhancement using image fusion", Procedia Comput. Sci., 46, pp. 1501-1509 (2015). 21. Zhou, Z., Sang, N., and Hu, X. Global brightness and local contrast adaptive enhancement for low illumination colour image", Optik, 125, pp. 1795-1799 (2014). 22. Raju, G. and Nair, M.S. A fast and e_cient colour image enhancement method based on fuzzy-logic and histogram", AEU-Int. J. Electron C., 68, pp. 237-243 (2014). 23. Chen, Y., Xiao, X., Liu, H., and Feng, P. Dynamic colour image resolution compensation under low light", Optik, 126, pp. 603-608 (2015). 24. Fu, X., Zeng, D., Huang, Y., Zhang, X., Ding, X., and Paisley, J. A fusion-based enhancing method for weakly illuminated images", Signal Process., 129, pp. 82-96 (2016). 25. Sun, X., Liu, H., Wu, S., Fang, Z., Li, C., and Yin, J. Low-light image enhancement based on guided image _ltering in gradient domain", Int. J. Digital Multimedia Broadcasting, 2017, Article ID 9029315 (2017). DOI:10.1155/2017/9029315 26. Guo, X., Li, Y., and Ling, H. LIME: low-light image enhancement via illumination map estimation", IEEE Trans. Image Process, 26(2), pp. 982-993 (2017). 27. Li, C., Guo, J., Porikli, F., and Pang, Y. LightenNet: a convolutional neural network for weakly illuminated image enhancement", Pattern Recog. Lett., 104, pp. 15-22 (2018). 28. Ng, M. and Wang, W. A total variation model for retinex", SIAM J. Imag. Sci., 4(1), pp. 345-365 (2011). 29. Zosso, D., Tran, G., and Osher, J.S. Non-local retinex- a unifying framework and beyond", SIAM J. Imaging. Sci., 8(2), pp. 787-826 (2015). 30. Retinex Image Processing, Nasa Langley Research Center, is available online in: http:// dragon.larc.nasa.gov/retinex/pao/news (2015). 31. Hassanpour, H., Darvishi, A., and Khalili, A. A regression-based approach for measuring similarity in discrete signals", Int. J. Electron, 98, pp. 1141-1156 (2011). 32. Asghari, M. and Alizadeh, S. A new similarity measure by combining formal concept analysis and clustering for case-based reasoning", Lect Notes Artif Int, 91(1), pp. 503-513 (2015). 33. Liese, F. and Vajda, I. On divergences and informations in statistics and information theory", IEEE T. Inform Theory, 52, pp. 4394-4412 (2006). 34. Lin, J. Divergence measure based on the Shannon entropy", IEEE T Inform Theory, 37(1), pp. 145-151 (1991). 35. Shahrizan Abdol Ghani, A. and Nor Ashidi Mat Isa Enhancement of low quality underwater image through integrated global and local contrast correction", Appl Soft Comput., 37, pp. 332-344 (2015). 36. Al-Ameen, Z., Sulong, G., Rehman, A., Al-Dhelaan, A., Saba, T., and Al-Rodhaan, M. An innovative technique for contrast enhancement of computed tomography images using normalized gammacorrected contrast-limited adaptive histogram equalization", Eurasip J. Adv. Sig. Pr., 32, pp. 1-12 (2015). 37. Ye, Z. Objective assessment of nonlinear segmentation approaches to gray level underwater images", Int. J. Graphycs Vision Image Process, 9, pp. 39-46 (2009). 38. Iqbal, K., Odetayo, M., James, A., Salam, R.A., and Talib, A.Z. Enhancing the low quality images using unsupervised color correction method", Int. Conf. on System Man and Cybernatics, Istanbul, Turkey, pp. 1703-1709 (2010). 39. Munteanu, C. and Rosa A. Gray-scale image enhancement as an automatic process driven by evolution", IEEE Trans. on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(2), pp. 1292-1298 (2004). 40. Bychkovsky, V., Paris, S., Chan, E., and Durand, F. Learning photographic global tonal adjustment with a database of input / output image pairs", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 97-104 (2011).