Neutron Imaging Restoration Based On Steering Kernel And Regularized Richardson-Lucy Algorithm

Neutron imaging as a non-destructive testing technology, has some certain characteristics compared with X rays and gamma ray imaging. Neutron image obtained by neutron imaging system carries the information such as the internal structure of the material being tested. Because of the comprehensive effect of neutron flux, spatial resolution of the beam collimation, detectors and scattering factors, the neutron image will inevitably occur serious degradation, mainly for image blur and noisy. Therefore,research on neutron image restoration method, on the one hand contribute to the degraded image restoration in structure of the content, it can also reduce the hardware requirements of the front-end imaging device.Richardson- Lucy algorithm(hereinafter referred to as the RL algorithm) is one of the widely used image restoration algorithm currently. Compared with other methods, RL algorithm can effectively recover degraded image details, but at the same time, RL algorithm will enlarge the noise of the image. In order to solve this problem, some studies by introducing regularization conditions(such as the total variational model) to improve the RL algorithm, get a TV-RL algorithm. Other studies introduced the local adaptive modulation kernel regression(SK) into the RL algorithm, by acquiring the structure information, so as to achieve the restoration of the degraded image. This article is presented based on kernel regression regularized RL algorithm, namely SK-TV-RL algorithm, and specifically applied on neutron image restoration. The relevant experimental results show that, compared to some of the existing recovery methods, both in the signal-to-noise ratio data and intuitive vision, the new method can provide more excellent recovery results.