Neutron Image Restoration by Combining Richardson-Lucy With Steering Kernel

Neutron image obtained from a small digital neutron imaging system, always has characteristics of low contrast, blurred edges and serious noise. It is disadvantageous to further analyse information about the sample’s internal structure, so it is essential for the observer to process the degraded image to improve its visual quality. In order to avoid the noise amplification problem of the original Richardson-Lucy (R-L) algorithm, which is adopted to recover degraded image, a restoration algorithm by combining R-L algorithm with Steering Kernel (S-K) algorithm for neutron image is presented in this paper. First S-K algorithm is applied to restrain the noise of the blurred noisy neutron image, as well as improving the signal-to-noise ratio of the image, and then R-L algorithm is used to reconstruct the blurred noisy image. The proposed algorithm is able to make up for the deficiency of R-L algorithm in dealing with the noise amplification problem, which is caused by the repeated iteration, while retaining the details of the image characteristics as much as possible. Comparative experimental results show that the algorithm can obtain satisfactory restoration visual effect for neutron image. The details of the work done are described in this paper.