Abstract

The aim of infrared and visible image fusion is to obtain an integrated image that contains obvious object information and high spatial resolution background information. The integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. To attain this purpose, a fusion algorithm based on multi-level Gaussian curvature filtering (MLGCF) image decomposition is proposed. First, a MLGCF is presented and employed to decompose the input source images into three different layers: small-scale, large-scale, and base layers. Then, three fusion strategies—max-value, integrated, and energy-based—are applied to combine the three types of layers, which are based on the different properties of the three types of layers. Finally, the fusion image is reconstructed by summing the three types of fused layers. Six groups of experiments demonstrate that the proposed algorithm performs effectively in most cases by subjective and objective evaluations and even exceeds many high-level fusion algorithms.

© 2019 Optical Society of America

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