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A multiscale contrast direction adaptation approach for the fusion of multispectral and multifocus infrared images

Infrared (IR) cameras are widely used in latest surveillance systems because spectral characteristics of objects provide valuable information for object detection and identification. To assist the surveillance system operator and automatic image processing tasks, fusing images in IR band is proposed as a solution to increase situational awareness and different fusion techniques are developed for this purpose. Proposed techniques are generally developed for specific scenarios because image content may vary dramatically depending on the spectral range, the optical properties of the cameras, the spectral characteristics of the scene, and the spatial resolution of the interested targets in the scene. A general purpose IR image fusion technique that is suitable for real-time applications is proposed. The proposed technique can support different scenarios by applying a multiscale detail detection and can be applied to images captured from different spectral regions of the spectrum by adaptively adjusting the contrast direction through cross checking between the source images. The feasibility of the proposed algorithm is demonstrated on registered multi-spectral and multi-focus IR images. Fusion results are presented and the performance of the proposed technique is compared with the baseline fusion methods through objective and subjective tests. The technique outperforms baseline methods in the subjective tests and provide promising results in objective quality metrics with an acceptable computational load. Besides, the proposed technique preserves object details and prevents undesired artifacts better than the baseline techniques in the image fusion scenario that contains four source images.