The invention discloses an
underwater robot imaging algorithm optimization method based on
deep learning, and relates to the technical field of
underwater robots, and the method comprises the steps: firstly carrying out the dynamic capture of an
underwater light field through employing a curved surface
CMOS array sensor, and adaptively adjusting the
focal length of the curved surface
CMOS array sensor according to the change of the
light intensity of a scene, and activating the local pixels when the scene changes; designing a
quantum convolution kernel, mapping classical
convolution operation into a
quantum circuit, constructing a
hybrid calculation pipeline, and performing
quantum state joint coding and cross-domain
feature fusion on optical and acoustic data to obtain fused data; through
quantum Fourier transform and quantum
convolution, data of different modalities are fused, the accuracy and robustness of imaging are improved, weights of optical images and
sonar data can be dynamically adjusted under different water
turbidity and illumination conditions, the fusion effect is improved, model errors are reduced, metasurface parameters can be adjusted in real time, sub-
wavelength wavefront regulation and control are achieved, and the method is suitable for large-scale popularization and application. And the adaptability and robustness of the imaging
system are improved.