Design of multi-objective guided filter infrared thermal image fusion for hypervelocity impact damages detection

Abstract

Spacecraft hypervelocity impact damage assessment is becoming increasingly important as the space debris environment deteriorates. Common single-target guided filtering methods are hardly capable of coping with the complex damage infrared thermal image fusion requirements of hypervelocity impacts. In this paper, a two-layer infrared reconstructed image fusion algorithm based on Multi-objective guided filtering (MOGF) is proposed. The aim is to keep the filtered input and output images similar while reducing thermal image noise and retaining as much of the tiny defects as possible in the fusion process. MOGF combines a multi-objective evolutionary optimization algorithm to simultaneously optimize multiple guided filter cost functions in the form of vectors in order to obtain the optimal set of guided filter weight parameters. Given the problem of irregular frontier surfaces in practical multi-objective optimization problems, a weight vector adaptive adjustment strategy is introduced to obtain a more uniform population. Finally, the optimal weight set is passed to the full pixel fusion layer to complete the complex damage infrared reconstructed image fusion. The experiments demonstrate that the proposed fusion method considering multiple targets has higher performance in hypervelocity impact complex damage fusion compared to the general single target fusion method.