Research on far-field spot search and centre location algorithms

Abstract

The energy distribution of the far-field spot is uneven, and the background is complex. Therefore, the identification of far-field points and the positioning of the centre are difficult. This article proposes an algorithm for multi-scale Gaussian cyclic convolution for locating the centre of far-field spots. First, wavelet denoising is performed on the image of multiple far-field spots, and the images of adjacent frames are subtracted. Then, the absolute values of the differences are added. Due to the large size of the far-field spot, the Gaussian distribution of the laser energy is not obvious. Therefore, a multi-scale Gaussian convolution kernel is used to perform circular convolution on these images. To preserve low-scale Gaussian features, features are spliced between two convolutions. In this article, we also design multi-angle combination filters to filter the enhanced Gaussian distribution features. Finally, curved polynomials are fitted to the reconstructed Gaussian energy distribution to obtain the maximum value; at this point, the position where the maximum value lies is the centre of the far-field spot. The experiments showed that this method has better robustness.