An image enhancement method for visual inspection of nuclear fuel assemblies

When underwater camera is used to carry out the visual inspection after fuel reloading in nuclear power plants, heat exchange between fuel assemblies and water can generate underwater turbulence, which causes imaging distortion. Turbulence severely affects core verification of nuclear fuel assemblies, serial number of which should be identified. With the aim to recover the images from a video sequence severely distorted by turbulence, an image enhancement method is proposed. At first, an image quality assessment metric FSIM is used to select the better quality frames. Next an iterative robust registration algorithm is used to eliminate most geometric deformations and recover the water surface. The temporal mean of the sequence is utilized to overcome the structured turbulence of the waves through the algorithm. Finally, the sparse errors are extracted from the sequence through rank minimization to remove unstructured sparse noise. After image processing, optical character recognition is performed by KNN and CNN, obtaining high recognition rates of 99.33%, 100% respectively. The experimental results show that the suggested method significantly performs better in distorted image restoration and image text recognition on the task of visual inspection of nuclear fuel assemblies.