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Learning Visual Representation of Underwater Acoustic Imagery Using Transformer-Based Style Transfer Method
Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering.
Nonlinear Intensity Underwater Sonar Image Matching Method Based on Phase Information and Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
Nonlinear Intensity Sonar Image Matching based on Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
A Matching Algorithm based on Image Attribute Transfer and Local Features for Underwater Acoustic and Optical Images
Additionally, the method is based on the combination of image depth semantic layer, and it could indirectly display the local feature matching relationship between original image pair, which provides a new solution to the underwater multi-sensor image matching problem.
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data
Subsea images measured by the side scan sonars (SSSs) are necessary visual data in the process of deep-sea exploration by using the autonomous underwater vehicles (AUVs).
Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method
This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images.
