TY - JOUR
T1 - 基于分类与最小卷积区域暗通道先验的水下图像恢复
AU - Guodong, Liu
AU - Lihui, Feng
AU - Jihua, Lu
AU - Jianmin, Cui
N1 - Publisher Copyright:
© 2023 Universitat zu Koln. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - color equalization
KW - estimation of optical model parameters
KW - image classification based on thresholds
KW - oceanic optics
KW - peak signal-to-noise ratio
KW - underwater color image quality evaluation
UR - http://www.scopus.com/inward/record.url?scp=85153731264&partnerID=8YFLogxK
U2 - 10.3788/LOP220651
DO - 10.3788/LOP220651
M3 - 文章
AN - SCOPUS:85153731264
SN - 1006-4125
VL - 60
JO - Laser and Optoelectronics Progress
JF - Laser and Optoelectronics Progress
IS - 4
M1 - 0401003
ER -