Underwater image enhancement based on unsupervised adaptive uncertainty distribution

Yuan Xu; Feng Li; Kaimin Shi; Peikun Li

doi:10.1117/12.3014202

30 April 2024 Underwater image enhancement based on unsupervised adaptive uncertainty distribution

Yuan Xu, Feng Li, Kaimin Shi, Peikun Li

Proceedings Volume 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition; 1315609 (2024) https://doi.org/10.1117/12.3014202
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

Due to the problems of color distortion and low contrast in the acquired underwater images, an underwater image enhancement algorithm based on unsupervised adaptive uncertainty distribution is proposed in this paper. Firstly, the fog removal module and three traditional enhancement algorithms are used to generate different underwater reference images. Then, a multi-color space stretching module guided by statistics is used to correct the color of the reference image and enhance the contrast of the image. Then the image detail features are obtained by the feature extractor. Finally, Progressive Adaptive Instance Normalization and Laplacian convolution are combined to enhance statistical features, enhance image edge texture and improve image visual effect. Experimental results show that the proposed method can effectively improve image contrast and color deviation, and compared with the existing algorithm, the peak signal-to-noise ratio index of the proposed algorithm is increased by 20.78%, the structural similarity index is increased by 4.23%, and the underwater color image evaluation index is increased by 10.06%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuan Xu, Feng Li, Kaimin Shi, and Peikun Li "Underwater image enhancement based on unsupervised adaptive uncertainty distribution", Proc. SPIE 13156, Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition, 1315609 (30 April 2024); https://doi.org/10.1117/12.3014202

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