  In the field of underwater vision research, image matching between the sonar
sensors and optical cameras has always been a challenging problem. Due to the
difference in the imaging mechanism between them, which are the gray value,
texture, contrast, etc. of the acoustic images and the optical images are also
variant in local locations, which makes the traditional matching method based
on the optical image invalid. Coupled with the difficulties and high costs of
underwater data acquisition, it further affects the research process of
acousto-optic data fusion technology. In order to maximize the use of
underwater sensor data and promote the development of multi-sensor information
fusion (MSIF), this study applies the image attribute transfer method based on
deep learning approach to solve the problem of acousto-optic image matching,
the core of which is to eliminate the imaging differences between them as much
as possible. At the same time, the advanced local feature descriptor is
introduced to solve the challenging acousto-optic matching problem.
Experimental results show that our proposed method could preprocess
acousto-optic images effectively and obtain accurate matching results.
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.
