Neutron Image Denoising Method Based on Adaptive New Wavelet Threshold Function

Abstract

Neutron radiography is an important nondestructive testing technique in the industrial field. In the process of neutron imaging, it is affected by ray interference, neutron scattering, statistical fluctuation of neutron fluence rate and electronic noise generated by electronic equipment, which will lead to the degradation of image quality. To solve this problem, this paper proposes a new wavelet threshold function denoising method based on particle swarm optimization (PSO) algorithm to reduce the influence of noise on the neutron image. The basic idea of this method is to combine PSO algorithm with the improved wavelet threshold function denoising. The new wavelet threshold function overcomes the problems of discontinuity in the traditional hard threshold function and fixed deviation in the wavelet coefficient of the traditional soft threshold function, and has the adjustment factor, which can combine the advantages of the traditional soft and hard threshold functions. PSO algorithm is used to find the optimal adjustment factor for image denoising. In addition, the new wavelet threshold function is continuous and smooth at the threshold, avoiding excessive strangulation of wavelet coefficients. The results of Matlab software experiments show that the new method can significantly improve the Peak Signal-to-Noise Ratio (PSNR) and reduce the Mean Square Error (MSE) of noisy images compared with the other four methods in removing Gaussian noise and Poisson noise. Therefore, the new method can retain more image details and effectively improve the quality of neutron images.

Keywords

• wavelet threshold function / particle swarm optimization algorithm / neutron image / image denoising