DBAC: A Novel Method for Grayscale Correction of Underwater Pipeline Images Using Side-Scan Sonar

Side-scan sonar (SSS) is a commonly used sensor device in underwater pipeline detection tasks. In the processing of SSS waterfall images for underwater pipeline detection, uneven grayscale often occurs due to factors such as sonar energy attenuation and underwater channel interference. Existing grayscale correction techniques struggle to meet the specific needs of underwater pipeline detection, as they fail to fully utilize pipeline characteristic information and have limitations in adapting to the complex underwater environment. To address these challenges, this paper proposes a dual-branch adaptive correction (DBAC) algorithm for adaptive grayscale correction of SSS images containing underwater pipelines. Unlike existing methods, this approach decomposes SSS images into low-frequency and high-frequency components using Shearlet transform. A pipeline-aware adaptive gamma correction (PAGC) algorithm and an adaptive dictionary update sparse dictionary learning (ADASDL) algorithm are designed to process these components accordingly. The PAGC algorithm uses pipeline contour and large-scale texture features in the low-frequency components as specific constraints to dynamically adjust the gamma value, enhancing the grayscale correction effect. Meanwhile, the noise in the high-frequency components is effectively reduced using the ADASDL algorithm. This innovative dual-branch strategy improves the adaptability of grayscale correction methods in complex underwater environments while making full use of pipeline feature information. Experimental results show that the proposed method outperforms four traditional algorithms and four newly proposed algorithms in terms of visual quality across six datasets, validating its effectiveness and innovation in grayscale correction for underwater pipeline side-scan sonar images.