Reconstructing the Colors of Underwater Images Based on the Color Mapping Strategy

Siyuan Wu, Bangyong Sun (), Xiao Yang, Wenjia Han, Jiahai Tan and Xiaomei Gao
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Siyuan Wu: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Bangyong Sun: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Xiao Yang: School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
Wenjia Han: Key Laboratory of Pulp and Paper Science, Technology of Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Jiahai Tan: School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, China
Xiaomei Gao: Xi’an Mapping and Printing of China National Administration of Coal Geology, Xi’an 710199, China

Mathematics, 2024, vol. 12, issue 13, 1-19

Abstract: Underwater imagery plays a vital role in ocean development and conservation efforts. However, underwater images often suffer from chromatic aberration and low contrast due to the attenuation and scattering of visible light in the complex medium of water. To address these issues, we propose an underwater image enhancement network called CM-Net, which utilizes color mapping techniques to remove noise and restore the natural brightness and colors of underwater images. Specifically, CM-Net consists of a three-step solution: adaptive color mapping (ACM), local enhancement (LE), and global generation (GG). Inspired by the principles of color gamut mapping, the ACM enhances the network’s adaptive response to regions with severe color attenuation. ACM enables the correction of the blue-green cast in underwater images by combining color constancy theory with the power of convolutional neural networks. To account for inconsistent attenuation in different channels and spatial regions, we designed a multi-head reinforcement module (MHR) in the LE step. The MHR enhances the network’s attention to channels and spatial regions with more pronounced attenuation, further improving contrast and saturation. Compared to the best candidate models on the EUVP and UIEB datasets, CM-Net improves PSNR by 18.1% and 6.5% and SSIM by 5.9% and 13.3%, respectively. At the same time, CIEDE2000 decreased by 25.6% and 1.3%.

Keywords: underwater imaging; color mapping; underwater image enhancement; color correction; color mapping (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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