A fusion approach to infrared and visible images with Gabor filter and sigmoid function

Abstract

The fusion of infrared (IR) and visible images should not only increase the brightness of the infrared targets, but also preserve more details in visible images. A fusion approach to infrared and visible images with Gabor filter and sigmoid function is proposed in this paper. In order to make targets prominent in the fused image, the IR image is normalized by the sigmoid function to get a mapping matrix W, and then the visible image is enhanced by the matrix W to obtain an enhanced visible image, so that the contrast between the target and the background in the visible image is enhanced. After the decomposition of the IR image, the visible image and the enhanced visible image with Gabor filter, the detail layers of the visible image and the enhanced visible image are fused using the “max-absolute” rule, and the base layers are fused using the rule of weighted summation, which greatly increases the amount of information in the visible image. The weighted summation is calculated for the basic layers of the infrared and fused visible image, and the absolute maximum value is calculated for their detail layers. Finally, the fused image is generated by a linear combination of the final detail layer and the final base layer. Nine existing algorithms with better performance and the algorithm proposed in this paper are tested on public datasets, and use six evaluation metrics such as average gradient (AG), cross entropy (CE), edge gradient (EI), information entropy (IE), peak signal-to-noise ratio (PSNR) and spatial frequency (SF) to evaluate the quality of the fused image. Experiments show that the new algorithm has achieved better visual effects, and most of the objective evaluation metrics are also better than other algorithms. In particular, the low-light image is enhanced by the sigmoid function and then fused, so that the fused image is clearer and the target is more prominent. The gradient information is retained in the fused image as much as possible. All these prove the advantage and effectiveness of the new algorithm.