A Perception-Aware Decomposition and Fusion Framework for Underwater Image Enhancement

• Published in: IEEE transactions on circuits and systems for video technology Vol. 33; no. 3; p. 1
• Main Authors: Kang, Yaozu, Jiang, Qiuping, Li, Chongyi, Ren, Wenqi, Liu, Hantao, Wang, Pengjun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Decomposition; Fuses; Image color analysis; Image contrast; Image enhancement; image fusion; Image quality; Image reconstruction; patch decomposition; Perception; Task analysis; Underwater; Underwater image; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2022.3208100

This paper presents a perception-aware decomposition and fusion framework for underwater image enhancement (UIE). Specifically, a general structural patch decomposition and fusion (SPDF) approach is introduced. SPDF is built upon the fusion of two complementary pre-processed inputs in a perception-aware and conceptually independent image space. First, a raw underwater image is pre-processed to produce two complementary versions including a contrast-corrected image and a detail-sharpened image. Then, each of them is decomposed into three conceptually independent components, i.e., mean intensity, contrast, and structure, via structural patch decomposition (SPD). Afterwards, the corresponding components are fused using tailored strategies. The three components after fusion are finally integrated via inverting the decomposition to reconstruct a final enhanced underwater image. The main advantage of SPDF is that two complementary pre-processed images are fused in a perception-aware and conceptually independent image space and the fusions of different components can be performed separately without any interactions and information loss. Comprehensive comparisons on two benchmark datasets demonstrate that SPDF outperforms several state-of-the-art UIE algorithms qualitatively and quantitatively. Moreover, the effectiveness of SPDF is also verified on another two relevant tasks, i.e., low-light image enhancement and single image dehazing. The code will be made available soon.