Feature-level fusion of dual-band infrared images based on gradient pyramid decomposition

Infrared thermal imager has been widely used in the fields of missile guidance and flaw detection. To identify the target clearly, the advanced one adopts dual bands sensors to capture images. Since of that, there is an urgent need of a fusion of the dual-bands images. The fused result includes much more exhaustive information than any single one, and can better reflect the actual. Among the algorithms used to fuse the dual-band infrared images, the weighted algorithm is the most widely used and easiest to be achieved. Nonetheless, its effect is not desired. We extract the features of the source images and make a fuse based on them on the feature-level. To get a better result, in this paper, the fusion strategy based on the Gradient pyramid transform has been mainly adopted. Meanwhile, there is a comparison with the weighted algorithm. Also, it makes an evaluation and analysis to the experimental data, and finally obtains the desired results.