Infrared weak-small target image fusion based on contrast and wavelet transform

The image details and contour information cannot be fully reflected for the current infrared single-band data. It is difficult for the weak-small target to resist background interference after imaging, so that the image produces a lower signal to noise ratio. Therefore, it is necessary to use the texture difference of different band data to improve the signal-to-noise ratio of the image through the complementary fusion method. In this paper, based on weak-small targets in infrared images under different backgrounds, the paper proposes a fusion method based on contrast and wavelet transform. Firstly, the source images are denoised, and multiscale two-dimensional decomposition are performed to obtain low-frequency component and high-frequency component. On this basis, the high-frequency component adopt the method of maximizing absolute value, and the low-frequency component use the method of weighted averaging. Then the image is reconstructed. Finally, the reconstructed image is fused by gray contrast modulation. The fusion results are compared with many fusion algorithms. The experimental results show that the proposed algorithm can improve the intensity of weak-small targets and easily identify weak-small targets in the image. It solves the background interference problem of weak-small targets in the image.