Neutron image denoising and deblurring based on generative adversarial networks

Abstract

Neutron radiography has been widely employed in nondestructive investigations. The noise and blur that are inevitably generated in the process of neutron radiography seriously decrease the image quality and lead to the loss of image information. In particular, white spot noise, which has the characteristics of nonuniform distribution, large size, and high magnitude, is difficult to remove. To solve this issue, we apply a generative adversarial network-based to convert a degraded neutron image into a clean image. The basic idea is to integrate attention mechanisms into our generation and discrimination networks. A fusion block with a visual attention mechanism is proposed, which can extract more potential features from the images and preserve the image texture as much as possible. In addition, due to the lack of neutron image datasets, we propose a neutron image degradation model to simulate the noise and blur in real neutron images. The results of the experiments show that our method can effectively eliminate noise and blur from neutron images while retaining texture information well.