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In the field of underwater vision research, image matching between the sonarsensors and optical cameras has always been a challenging problem. Due to thedifference in the imaging mechanism between them, which are the gray value,texture, contrast, etc. of the acoustic images and the optical images are alsovariant in local locations, which makes the traditional matching method basedon the optical image invalid. Coupled with the difficulties and high costs ofunderwater data acquisition, it further affects the research process ofacousto-optic data fusion technology. In order to maximize the use ofunderwater sensor data and promote the development of multi-sensor informationfusion (MSIF), this study applies the image attribute transfer method based ondeep learning approach to solve the problem of acousto-optic image matching,the core of which is to eliminate the imaging differences between them as muchas possible. At the same time, the advanced local feature descriptor isintroduced to solve the challenging acousto-optic matching problem.Experimental results show that our proposed method could preprocessacousto-optic images effectively and obtain accurate matching results.Additionally, the method is based on the combination of image depth semanticlayer, and it could indirectly display the local feature matching relationshipbetween original image pair, which provides a new solution to the underwatermulti-sensor image matching problem.

A Matching Algorithm based on Image Attribute Transfer and Local Features for Underwater Acoustic and Optical Images