A Novel Image Fusion Metric for Intelligent Manufacturing Information System

Abstract

An image fusion metric is commonly used to evaluate a fusion scheme because subjective evaluation cannot work in an intelligent manufacturing information system. In this study, an objective image fusion metric based on a log-Gabor filter (LGIMF) is presented. This metric can be calculated in five steps: (1) filtering the source and fused images into sub-bands, (2) constructing an ideal synthesis image by applying the maximization principle from the sub-band of the source images, (3) capturing the variation information between the real fused image and the ideal synthesis image in each sub-band, (4) measuring the sub-band fusion visual information by using the signalto- noise ratio model, and (5) weighting the sub-band fusion visual information to determine the overall quality. In our experiment, the proposed fusion metric is compared with other well-known metrics by using a subjective test dataset. We found that the LGIMF was more consistent in subjective perception compared with the other metrics.