基于分类与最小卷积区域暗通道先验的水下图像恢复

Translated title of the contribution: Underwater Image Restoration Based on Classification and Dark Channel Prior with Minimum Convolutional Area

Liu Guodong, Feng Lihui*, Lu Jihua, Cui Jianmin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To address the issue of picture blur and color distortion in underwater images of complex water bodies, an underwater image restoration algorithm based on HSV classification, CIELAB equalization, and minimum convolution region dark channel prior (DCP) is proposed. By the thresholds of H and S, the underwater photos are separated into high saturation distortion, low saturation distortion, and shallow water images. Then, the underwater image is recovered using CIELAB equilibrium and adaptive image enhancement, where the system parameters of the categorized underwater image are estimated by minimum convolutional area DCP. The experimental findings demonstrate that the suggested solution is superior to the comparison algorithms in image restoration effect, evaluation quality, and real-time performance indicators. The average peak signal-to-noise ratio and structural similarity values are increased by 26. 88% and 17. 3% on average, respectively, and the underwater image quality measurement value is increased by 4. 3%.

Translated title of the contributionUnderwater Image Restoration Based on Classification and Dark Channel Prior with Minimum Convolutional Area
Original languageChinese (Traditional)
Article number0401003
JournalLaser and Optoelectronics Progress
Volume60
Issue number4
DOIs
Publication statusPublished - 2023

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