The detection method of sidelobe peaks parameter in weak signal regions based on NVPCA image enhancement

Abstract

Aiming at the problem that the measurement of strong laser far-field focal spot based on side lobe beam diffraction inversion cannot extract the smallest measurable signal in the periphery of the side lobe image, this paper proposes a method for detecting the peak parameters of the weak signal region in the side lobe based on neighborhood vector principal component analysis (NVPCA) image enhancement. The main optimization measures are as follows: First, treat each pixel in the sidelobe image and its 8 neighborhood pixels as a column vector to construct a 9-dimensional data cube, and the first dimensional data after PCA transformation is an NVPCA image; Secondly, using angle transformation to transform the detection object, the various peak parameters of the one-dimensional sidelobe curve in all directions are detected, thereby obtaining the quantified energy distribution characteristics of the weak signal area of the sidelobe beam; Then, search for the maximum position points of each sidelobe peak in all directions, connect all position points to generate a maximum ring for each sidelobe peak, and calculate the grayscale mean of each maximum ring; Finally, the grayscale mean of the maximum rings that is greater than the LCM target separation threshold and the smallest is selected as the minimum measurable sidelobe peak signal of the entire sidelobe beam. The experimental results show that the sidelobe weak signal detection method based on NVPCA image enhancement can separate and extract the minimum measurable signal from the fifth peak ring on the periphery of the sidelobe image, increasing the dynamic range ratio by 1.528 times, improving the calculation accuracy of the dynamic range ratio, and laying a foundation for the future accurate measurement of strong laser far field in large scientific devices.