Adaptive image fusion algorithm based on human visual system guided gradient transfer and total variation minimization

Xiaoqing Luo; Chenchen Yuan; Zhancheng Zhang

doi:10.1117/1.JEI.27.5.053039

15 October 2018 Adaptive image fusion algorithm based on human visual system guided gradient transfer and total variation minimization

Xiaoqing Luo, Chenchen Yuan, Zhancheng Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053039 (October 2018). https://doi.org/10.1117/1.JEI.27.5.053039

Abstract

To effectively explore image content for image fusion and generate a full end-to-end image fusion pipeline, a parameter adaption algorithm based on gradient transfer and total variation (TV) minimization is proposed, and the fusion problem is transformed into a human visual system guided TV minimization problem, where the data fidelity term maintains the structure edge features of the image, and the regularization term preserves the gradient variation texture detail information. Then, the fusion problem is cast into the optimization of minimum energy functional model. To facilitate choice of regularization parameter, a modified fixed-point iterative method is employed to solve the optimization. Extensive experiments show that this model can obtain both rich detail and spectral information; adjusting the parameters of the proposed adaptive selection strategy can effectively improve the fusion effect while making the image fusion process work as an end-to-end pipeline. Compared to eight state-of-the-art fusion methods, the experimental results demonstrate the effectiveness of the proposed method.

Citation Download Citation

Xiaoqing Luo, Chenchen Yuan, and Zhancheng Zhang "Adaptive image fusion algorithm based on human visual system guided gradient transfer and total variation minimization," Journal of Electronic Imaging 27(5), 053039 (15 October 2018). https://doi.org/10.1117/1.JEI.27.5.053039

Received: 26 January 2018; Accepted: 12 September 2018; Published: 15 October 2018

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