Underwater distorted image restoration algorithm based on compressed sensing and centroid method

Imaging of static scenes under water is widely used in underwater detection technology. Aiming at the problem of severe distortion of underwater scene images caused by water surface fluctuations. An underwater distorted image restoration algorithm based on compressed sensing and centroid method is proposed. First, track the significant feature points between video frames to obtain the displacement trajectory of the feature points, then use the compressed sensing algorithm to reconstruct the motion vector field of the entire image to eliminate the periodic distortion of the image. Further, on this basis, the dense optical flow between the images is calculated to construct the centroid of the image sequence, thereby eliminating the randomness distortion that may exist in the image. The experimental results show that compared with the current mainstream restoration methods, the algorithm can ensure that the restored image has high definition while eliminating image distortion, and it performs well in some image quality evaluation indicators.