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Sonar Image Quality Improvement for Target Detection Based on Seafloor Reflection Model
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DOI：10.1109/lgrs.2026.3710631.png)
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Abstract
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Side scan sonar (SSS) is the fundamental equipment for underwater target detection. Whether the traditional detection methods or the deep learning-based methods, the detection results are closely related to the image quality. However, most of the existing methods for improving image quality are mainly focused on enhancing image quality from the perspective of image processing, and rarely consider the sonar imaging mechanism. In this letter, we proposed a new method for improving the sonar image quality based on the seafloor reflection model, and evaluated the image quality using the reference-free image quality evaluation index and the comparison of target detection results. Experimental results proved that the sonar image quality is better in terms of image entropy, image contrast, and average gradient after using the proposed method. Meanwhile, using the improved images for target detection, the accuracy performed better. The experiments proved that this proposed method is valid and transferable.
Keywords:
Quality
reflection model
sonar image
target detection
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