A multi-band image synchronous fusion method based on saliency

• We use CWF to decompose the image into base and detail layers, which enables the target edges of the fused image to be more clearly. • We use gamma correction for IR image salient target extraction and propose a method to calculate parameters adaptively according to the image content to make it more suitable for base layer fusion. Compared with the traditional “average” fusion rule, the proposed base layer fusion method can not only retain the salient targets of IR images well but also retain the rich background information of near-infrared (NIR) and VIS images. • We propose an optimization model for synchronous fusion and noise reduction in the detail layer and solve it with the alternating direction method of multipliers (ADMM). Compared with other detail layer fusion methods, the fusion results of the algorithm proposed in this study are closer to good quality and informative images, and thus it can reduce the influence of noisy images on the fusion results. A new saliency-based multi-band image synchronous fusion method is proposed. First, combined window filtering is used to decompose the image into base and detail layers to better protect the edges of the image. Second, gamma correction is used for infrared image salient region extraction. To make it more applicable to the base layer fusion, we propose a method to calculate the parameters adaptively according to the image content. Consequently, the fusion result can better retain the thermal radiation information of the target. We then consider both the image quality and the amount of information contained in the image and construct an optimization model for synchronous fusion and noise reduction of detail layers to reduce the influence of noisy images on the fusion results. Finally, the fused base and detail layers are combined to obtain the final image. Our proposed method is evaluated and compared with representative methods, both qualitatively and quantitatively, applied to the TNO dataset. The results demonstrate that the proposed method aligns with human visual observation in highlighting salient targets and retaining valid detail information. The quality metrics are better than the comparison methods on the whole.