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Solid waste surface feature enhancement method based on gamma correction and wavelet transform

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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

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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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