Application of neural network methods in the synthesis of sonar images

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Abstract

The study examines the effectiveness of neural network-based methods for the synthesis of sonar images. An experimental evaluation was conducted to assess the performance of the neural style transfer method, the deep painterly harmonization method, and the arbitrary style transfer method with adaptive normalization. An original modification of the neural style transfer method is proposed, which demonstrated superior effectiveness compared to other methods in generating sonar images.

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About the authors

A. S. Mironov

Pacific National University

Author for correspondence.
Email: andrei.s.mironov@yandex.ru

Cand. Of Tech. Sc., Assistant Professor

Russian Federation, Khabarovsk, 680035

A. A. Saenko

Pacific National University

Email: saa2410@mail.ru

Postgraduate Student, Assistant lecturer

Russian Federation, Khabarovsk, 680035

E. S. Fomina

Pacific National University

Email: fominaekt@gmail.com

Senior Lecturer

Russian Federation, Khabarovsk, 680035

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Supplementary files

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2. Fig. 1. Examples of sonar images

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3. Fig. 2. Neural style transfer method with different input parameters

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4. Fig. 3. Style transfer algorithm

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5. Fig. 4. Result of the deep painterly harmonization method

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6. Fig. 5. Style application algorithm

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7. Fig. 6. Result of the arbitrary style transfer method with adaptive normalization

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8. Fig. 7. Comparison of various methods: a — part of the original GBO image; б — GBO image with content; в — classical neural style transfer method (see Fig. 2, a); г — deep painterly harmonization method; д — arbitrary style transfer method with adaptive normalization; е — the authors' neural style transfer method (see Fig. 2, б)

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