INTENSITY CORRECTION IN SIDE SCAN SONAR IMAGES. METHODS OVERVIEW
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.3.2.27Keywords:
side-scan sonar, sonogram, intensity correction, sonar mosaic, stripe noise, digital signal processing, adaptive algorithms, deep learning, computer visionAbstract
The use of sonars, particularly side-scan sonars, in underwater research dates back to the mid-20th century. Due to their relative affordability, ease of operation, and high efficiency in seabed visualization, these devices have become indispensable tools in hydrography, marine archaeology, environmental monitoring, and search-and-rescue operations.However, the processing of sonar images (sonograms) presents a number of challenges caused by signal distortions. The main types of such artifacts include intensity non-uniformities, stripe noise, geometric distortions, and residual effects of imperfect time-based intensity compensation. Given the complexity of acoustic propagation in underwater environments and the variability of sensor configurations, there is currently no universal method for intensity correction that performs effectively across all scenarios. This study presents a structured review of existing methods for correcting intensity in side- scan sonar images developed over recent decades. Emphasis is placed on their algorithmic implementation, suitability for real-time processing, and effectiveness in constructing high-quality sonar mosaics. Particular attention is paid to the analysis of the causes of intensity variation, including the phenomenon of brightness falloff across the swath, repetitive stripe noise, time-varying gain, and residual intensity anomalies in the time domain. The review covers a range of models and approaches, including empirical smoothing techniques, multivariate regression, local normalization, hybrid filtering strategies, and methods based on physical models of acoustic scattering. A proposed classification framework allows for the organization of these approaches according to several criteria: model type (empirical, physical, machine learning), underlying assumptions, constraints under real-world conditions, and the types of metrics used for quality evaluation.The potential of each method to be adapted for tasks such as automatic object detection and the construction of accurate seafloor morphology models is also explored. This material may be valuable both for engineering practitioners involved in applied sonar processing and for researchers seeking advanced algorithms for sonogram enhancement and develop adaptive computer vision systems for complex underwater environments.
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