NVPCA Image Enhancement-Based Detection Method for Sidelobe Peak Parameters in Weak Signal Regions
Objective The primary application of the host device involves research in high-energy density physics and inertial confinement fusion,handling energies up to 100000 joules.A significant challenge encountered during these experiments is the simultaneous detection of strong and weak signals in the far-field focal spot.Specifically,accurately measuring weak signals in the sidelobe area of the far-field focal spot has proven difficult.To address this,we introduce a peak parameter detection method for weak signal regions in the sidelobe,leveraging neighborhood vector principal component analysis(NVPCA)for image enhancement.Methods Our optimization strategy includes several steps.First,we treat each pixel in the sidelobe image and its eight neighboring pixels as a column vector to construct a 9-dimensional data cube.The first dimension post-PCA transformation,the NVPCA image,is then selected.Next,we employ angle transformation to detect various peak parameters of the one-dimensional sidelobe curve in all directions,facilitating the quantification of energy distribution in the sidelobe's weak signal area.Subsequently,we identify the maximum position points of each sidelobe peak in all directions,linking these to form a maximum ring for each peak and calculating the grayscale mean of these rings.The smallest grayscale mean exceeding the LCM target separation threshold is identified as the minimum measurable signal for the entire sidelobe beam.Results and Discussions 1)We propose a sidelobe weak signal detection method using NVPCA image enhancement.This approach successfully isolates and extracts the minimum measurable signal from the 5th peak ring on the sidelobe image's periphery,increasing the dynamic range ratio to 1.528 times.This method enhances the peak's maximum value in any direction,ensuring the extraction of the minimum measurable signal from the peripheral 5th peak loop.2)The LCM target detection threshold formula is employed to segregate the minimum measurable signal.This formula,tailored to the characteristics of far-field focal lobe images,effectively separates background noise.3)We validate the one-dimensional curve peak parameters in various directions using a two-dimensional plane display method.Combining two-dimensional and one-dimensional displays,this method not only showcases the peak parameter distribution of one-dimensional sidelobe curves from multiple perspectives but also differentiates adjacent sampling angles'peak positions.The validation using equations(11)-(13)yields rising edge,falling edge,and pulse width consistent with those in Table 5,confirming the two-dimensional display method's efficacy in verifying one-dimensional curve peak parameters.Conclusions Addressing the challenge of extracting the smallest measurable signal in the sidelobe image's periphery for strong laser far-field focal spot measurements,we introduce a sidelobe weak signal region peak parameter detection method based on NVPCA image enhancement.Our findings demonstrate this method's capability to isolate and extract the minimum measurable signal from sidelobe image peripheral peaks,increasing the dynamic range ratio to 1.528 times.This approach is crucial for accurately measuring weak signal areas in sidelobe beams,understanding their energy distribution,and laying the groundwork for future precise measurements of strong laser far-field focal spots in large-scale laser devices.
far-field measurementneighborhood vector principal component analysisdiffraction inversion of sidelobe beamangle transformationparameter detection of sidelobe peaks
万方数据
维普
