Abstract
Backlit images are a combination of dark and bright regions and the objects in the image generally appear to be dark for human perception. The region of interest (ROI) in general confines to the object(s) present in the image or some regions of the image. Such ROI in backlit images have low contrast and it is difficult for visualization. Enhancement of ROI in backlit images is necessary in order to view the contents properly. In this paper, a novel and simple approach for the enhancement of ROI of backlit images is proposed. This approach considers several features including tone mappings, exposedness, gradient, median filtering, etc. and finally, the fusion of the results has been done. The novel contribution in the proposed method, though seems to be trivial, attained best results without applying pyramid based operations namely Laplacian pyramid and Gaussian pyramid. Efficacy of the proposed method is evident from the experimental results which confirm that the proposed approach gives better results both qualitatively (visualization) and quantitatively (objective evaluation) compared to the existing methods.
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References
Buades, A., Lisani, J.L., Petro, A.B., Sbert, C.: Backlit images enhancement using global tone mappings and image fusion. IET Image Process. 14(2), 211–219 (2019)
Celik, T.: Spatial entropy-based global and local image contrast enhancement. IEEE Trans. Image Process. 23(12), 5298–5308 (2014)
Chouhan, R., Biswas, P.K., Jha, R.K.: Enhancement of low-contrast images by internal noise-induced fourier coefficient rooting. Sign. Image Video Process. 9(1), 255–263 (2015)
Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., Paisley, J.: A fusion-based enhancing method for weakly illuminated images. Sign. Process. 129, 82–96 (2016)
Gottschlich, C.: Curved-region-based ridge frequency estimation and curved gabor filters for fingerprint image enhancement. IEEE Trans. Image Process. 21(4), 2220–2227 (2011)
Hessel, C.: An implementation of the exposure fusion algorithm. Image Process. OnLine 8, 369–387 (2018)
Huang, H., Tao, H., Wang, H.: A convolutional neural network based method for low-illumination image enhancement. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition, pp. 72–77 (2019)
Im, J., Yoon, I., Hayes, M.H., Paik, J.: Dark channel prior-based spatially adaptive contrast enhancement for back lighting compensation. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2464–2468. IEEE (2013)
Jha, R.K., Chouhan, R., Aizawa, K., Biswas, P.K.: Dark and low-contrast image enhancement using dynamic stochastic resonance in discrete cosine transform domain. APSIPA Transactions on Signal and Information Processing, vol. 2 (2013)
Li, C., Liu, J., Liu, A., Wu, Q., Bi, L.: Global and adaptive contrast enhancement for low illumination gray images. IEEE Access 7, 163395–163411 (2019)
Li, Z., Wu, X.: Learning-based restoration of backlit images. IEEE Trans. Image Process. 27(2), 976–986 (2018)
Liu, S., Zhang, Y.: Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans. Consum. Electron. 65(3), 303–311 (2019)
Martorell, O., Sbert, C., Buades, A.: Ghosting-free dct based multi-exposure image fusion. Sign. Process. Image Commun. 78, 409–425 (2019)
Mertens, T., Kautz, J., Van Reeth, F.: Exposure fusion: a simple and practical alternative to high dynamic range photography. In: Computer Graphics Forum, vol. 28, pp. 161–171. Wiley Online Library (2009)
Morel, J.M., Petro, A.B., Sbert, C.: Screened poisson equation for image contrast enhancement. Image Process. OnLine 4, 16–29 (2014)
Niu, Y., Wu, X., Shi, G.: Image enhancement by entropy maximization and quantization resolution upconversion. IEEE Trans. Image Process. 25(10), 4815–4828 (2016)
Pizer, S.M., et al.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
Ren, Y., Ying, Z., Li, T.H., Li, G.: Lecarm: low-light image enhancement using the camera response model. IEEE Trans. Circ. Syst. Video Technol. 29(4), 968–981 (2018)
Rivera, A.R., Ryu, B., Chae, O.: Content-aware dark image enhancement through channel division. IEEE Trans. Image Process. 21(9), 3967–3980 (2012)
Singh, H., Kumar, V., Bhooshan, S.: A novel approach for detail-enhanced exposure fusion using guided filter. The Scientific World Journal, vol. 2014 (2014)
Wang, Q., Fu, X., Zhang, X.P., Ding, X.: A fusion-based method for single backlit image enhancement. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 4077–4081. IEEE (2016)
Wang, S., Luo, G.: Naturalness preserved image enhancement using a priori multi-layer lightness statistics. IEEE Trans. Image Process. 27(2), 938–948 (2017)
Wang, W., Wu, X., Yuan, X., Gao, Z.: An experiment-based review of low-light image enhancement methods. IEEE Access 8, 87884–87917 (2020)
Wang, Y.F., Liu, H.M., Fu, Z.W.: Low-light image enhancement via the absorption light scattering model. IEEE Trans. Image Process. 28(11), 5679–5690 (2019)
Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: A new low-light image enhancement algorithm using camera response model. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 3015–3022 (2017)
Zarie, M., Pourmohammad, A., Hajghassem, H.: Image contrast enhancement using triple clipped dynamic histogram equalisation based on standard deviation. IET Image Process. 13(7), 1081–1089 (2019)
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Yadav, G., Yadav, D.K., Mouli, P.V.S.S.R.C. (2021). Enhancement of Region of Interest from a Single Backlit Image with Multiple Features. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_39
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