Abstract
The solid waste images obtained by the machine vision recognition system have many problems, such as uneven illumination, low contrast, and not apparent surface features, seriously affecting the accuracy of surface feature recognition. A method of combining gamma correction and wavelet transform is proposed to enhance the details of images. Firstly, the original image is transformed into hue-saturation-intensity color space, and the guided filtering method is used to separate the illumination form intensity components. An improved two-dimensional gamma function is constructed to balance brightness and enhance contrast. The gamma correction parameter is dynamically adjusted according to the distribution of illumination. Secondly, the wavelet transform is used to convert the corrected image into the frequency domain. An improved wavelet threshold algorithm is proposed to remove the noise introduced by gamma correction. Lastly, the wavelet transform is used to restructure intensity and convert the image to initial color space. Experimental results demonstrate that the proposed method could enhance the details of images under several different illumination conditions.
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Abbreviations
- \(I_\mathrm{inty}\) :
-
The image of intensity component
- \(I_\mathrm{gamma}\) :
-
The image of gamma correction
- \(I_\mathrm{gauss}\) :
-
The image with gauss noise
- HSI:
-
Hue-saturation-intensity
- HE:
-
Histogram equalization
- WT:
-
Wavelet transform
- FIPSO:
-
Fuzzy based improved particle swarm optimization
- CVR-WT:
-
Curvature variation regularization wavelet transform
- PSNR:
-
Peak signal-to-noise ratio
- FDAHE-GC:
-
Fuzzy Dissimilarity Adaptive Histogram Equalization with Gamma Correction
- DTVR:
-
Directional Total Variation Retinex
References
Gundupalli, S.P., Hait, S., Thakur, A.: A review on automated sorting of source-separated municipal solid waste for recycling. Waste Manag. 60, 56–74 (2017)
Bo, T., Jianyi, K., Shiqian, W.: Review of surface defect detection based on machine vision. J. Image Graph. 22(12), 1640–1663 (2017)
Bora, D.J.: Importance of image enhancement techniques in color image segmentation: a comprehensive and comparative study. Indian J. Sci. Res. 15(1), 115–131 (2017)
Kong, N.S.P., Ibrahim, H.: Color image enhancement using brightness preserving dynamic histogram equalization. IEEE Trans. Consum. Electron. 54, 1962–1968 (2008)
Huang, S.-C., Cheng, F.-C., Chiu, Y.-S.: Efficient contrast enhancement using adaptive Gamma correction with weighting distribution. IEEE Trans. Image Process. 22, 1032–1041 (2013)
Wei, Z., Jun, W., Lidong, H., Zebin, S.: Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Process. 9, 908–915 (2015)
Sun, Q.Y., Wu, Q.X., Wang, X., Hou, L.: A spiking neural network for extraction of features in colour opponent visual pathways and FPGA implementation. Neurocomputing 228, 119–132 (2017)
Dhal, K.G., Das, A., Ray, S., et al.: Histogram equalization variants as optimization problems: a review. Arch. Comput. Methods Eng. 66, 1–26 (2020)
Rao, B.S.: Dynamic histogram equalization for contrast enhancement for digital images. Appl. Soft Comput. 89, 106114 (2020)
Subramani, B., Veluchamy, M.: Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement. Color Res. Appl. 45, 644–655 (2020)
Zhou, X., Wu, T.: A kind of wavelet transform image denoising method based on curvature variation regularization. ACTA Electron. Sin. 46(3), 621–628 (2018)
Kimmel, R., Elad, M., Shaked, D., et al.: A variational framework for retinex. Int. J. Comput. Vis. 52, 17 (2003)
Zosso, D., Tran, G., Osher, S.J.: Non-local retinex—a unifying framework and beyond. SIAM J. Imaging Sci. 8, 787–826 (2015)
Dianwei, W.A.N.G., Jing, W.A.N.G.: Adaptive correction algorithm for non-uniform illumination images. Syst. Eng. Electron. 39(6), 1383–1390 (2017)
Zhang, J., Zhou, P.: Low-light image enhancement based on directional total variation retinex. J. Comput. Aided Des. Comput. Graph. 30(10), 1943–1953 (2018)
Xu, Y., Duan, F.: Color space transformation and object oriented based information extraction of aerial images. In: 2013 21st International Conference on Geoinformatics, pp. 1–4 (2013)
He, K., Sun, J.: Tang X guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1397–1409 (2013)
He, K., Sun, J.: Fast Guided Filter, arXiv:1505.00996 (2015)
Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., et al.: An adaptive gamma correction for image enhancement. EURASIP J. Image Video Process. 2016(1), 1–13 (2016)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36, 961–1005 (1990)
Donoho, D.L.: De-noising by soft-thresholding. IEEE Tran. Inf. Theory 41, 613–627 (1995)
Lin, L., Feng, L.: Comparative analysis of image denoising methods based on wavelet transform and threshold functions. Int. J. Eng. 30(2), 199–206 (2017)
Veluchamy, M., Subramani, B.: Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction. Appl. Soft Comput. 89, 106077 (2020)
Acknowledgements
This work was supported by a grant from the Key R&D Projects (Social Development) in Jiangsu Province (Project Number is BE2018722). Further, we would like to thank Professor Wenhua. Ye for his revision of this article. We also thank the fellow students who gave a lot of help in the experiment.
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Ma, T., Ye, W., Leng, S. et al. Solid waste surface feature enhancement method based on gamma correction and wavelet transform. SIViP 15, 1627–1634 (2021). https://doi.org/10.1007/s11760-021-01898-2
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DOI: https://doi.org/10.1007/s11760-021-01898-2