Automatic evaluation of vertex structural defects on the anode surface of a low-light-level image intensifier based on proposed individual image processing strategies.

Abstract

In the process of microchannel plate (MCP) making and physicochemical treatment of a low-light-level (LLL) image intensifier, multifilament fixed pattern noise, also known as structural defects, is one of the most common defects in the anode surface. The appearance of this defect will seriously affect the imaging quality of an image intensifier, so it should be found in time before delivery. The traditional evaluation method of this defect relies on subjective judgment, and the disadvantage is that the division of the dense defect area and the measurement of defect gray difference (GD) are not standardized. To address this problem, an automatic evaluation method of vertex structural defects of an LLL image intensifier based on proposed individual image processing strategies is presented, which provides a digital evaluation scheme for such defects. This method is composed of two parts: quasi-circular defect detection and defect GD calculation. The first part is composed of coarse detection and fine detection. Coarse detection is to scan the anode surface and take the two ends of a pair of adjacent line segments with a large gradient sum and opposite gray change direction as the defect boundaries; fine detection is to establish the image patch from defect boundaries, extract the edge segment from the image patch, and judge whether it conforms to the shape of a circle. In order to substantiate the performance of the quasi-circular defect detection strategy, two relevant techniques are used as comparison. One is based on a Gaussian filter, and the other is based on a fixed-size window template. The comparison results show that our method, to the best of our knowledge, has the best detection performance for vertex structural defects. The second part consists of region of interest (ROI) cropping, secondary defect detection, shortest distance sequence establishment, effective distance extraction, triplet set construction, and triplet GD calculation. First, the location histogram of defects is established to cut ROI; then, the secondary defect detection is performed to extract more vertex structural defects from ROI; after that, the shortest distance sequence of defects is constructed, and the effective distances are extracted by using the structural features of multifilament. Finally, the triplet set is generated according to the effective distance, and the triplet GD is calculated based on the gray information near the triplet baseline. The GD of vertex structural defects corresponds to the maximum GD of triplets. So as to verify the effectiveness of vertex defect GD calculation strategy, several image tubes with different degrees of such defects are used for experiments, and the subjective evaluation method is used as comparison. The experimental results substantiate that this method is superior to the subjective method in locating ROI accurately and calculating defect GD quantitatively. In general, the automatic evaluation method can be regarded as an effective evaluation scheme for vertex structural defects of an LLL image intensifier.