Video Signal-Dependent Noise Estimation Via inter-Frame Prediction

We propose an infrared and visible image fusion algorithm using bimodal transformers. First, the proposed algorithm extracts multiscale features of the input infrared and visible images. Then, we develop the bimodal transformers that refine the extracted features by estimating their irrelevance maps to exploit the complementary information of the source images. Finally, we develop a reconstruction block that generates the fusion result by merging the refined features in the frequency domain to exploit the global information of the source images. Experimental results show that the proposed algorithm outperforms state-of-the-art infrared and visible image fusion algorithms on several datasets.