Underwater Image Enhancement via Principal Component Fusion of Foreground and Background

• Published in: IEEE transactions on circuits and systems for video technology Vol. 34; no. 11; pp. 10930 - 10943
• Main Authors: Zhang, Weidong, Liu, Qingmin, Feng, Yikun, Cai, Lei, Zhuang, Peixian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: background dehazed; Circuits and systems; Color; foreground contrast-enhanced; Image color analysis; Image contrast; Image degradation; Image enhancement; Image quality; Image reconstruction; Image restoration; Image transmission; Imaging; Marine resources; Multilayers; Principal component analysis; principal component fusion; Principal components analysis; Underwater; Underwater visual perception; Visual perception
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2024.3412748

Underwater imaging systems have evolved into essential hardware equipment for developing and utilizing marine resources. However, the complex underwater physical environment has often led to severe quality degradation of underwater visual perception. To address these issues, we design a principal component fusion method of foreground and background to enhance an underwater image, named PCFB. Specifically, we present a color balance-guided color correction strategy to remove color distortion issues that equalize the pixel values of the a and b channels of the CIELab color model. Subsequently, we implement a percentile maximum-based contrast enhancement strategy and a multilayer transmission map estimated dehazing strategy on the color-corrected image to yield the contrast-enhanced foreground and dehazed background sub-images. Finally, we employ a principal component analysis fusion method to reconstruct a high-visibility underwater image by integrating the advantages of the foreground contrast-enhanced sub-image and the background dehazed sub-image. Comprehensive experiments on three datasets demonstrate that our PCFB surpasses state-of-the-art methods both qualitatively and quantitatively. Moreover, our PCFB exhibits outstanding generalization capabilities for addressing haze and low-light images. The code is publicly available at: https://www.researchgate.net/publication/381259520_2024-PCFB .