Abstract
The dark channel prior dehazing algorithm can clear the foggy images to different degrees. However, the algorithm still has some deficiencies, such as the halo phenomenon at the edge of the image in the area of sudden change in depth of field; inaccurate estimation of transmittance, resulting in color shift in the image; inaccurate estimation of atmospheric light value, resulting in darker images after defogging, etc. Therefore, it is necessary to improve the dark channel prior algorithm. This paper improves the dehazing algorithm on the basis of in-depth study of the dark channel prior algorithm, and proposes a new dehazing algorithm based on the atmospheric scattering model. The simulation results show that the improved algorithm in this paper can effectively suppress the halo and color distortion in the abrupt depth of field area, and the obtained defogged image has rich detail information, clearer image, and moderate brightness. The algorithm has improved in objective parameters such as average gradient, structural similarity, peak signal-to-noise ratio and information entropy.
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Acknowledgement
This work was supported by the National Nature Science Foundation of China (62076249), Key Research and Development Plan of Shandong Province (2020CXGC010701, 2020LYS11), Natural Science Foundation of Shandong Province (ZR2020MF154).
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Pan, X., Wang, H., Liu, Y., Zhao, Z., Huang, F. (2023). Research on Improved Image Dehazing Algorithm Based on Dark Channel Prior. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_31
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