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A measure for the evaluation of multi-focus image fusion at feature level

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Abstract

Most multi-focus image fusion evaluation methods are based on focus detection and measuring the similarity between the fused image and the whole source images including defocused regions, which is liable to result in the difference between the evaluation result and the real image fusion quality. To overcome the problem above, we proposed a novel objective measure for multi-focus image fusion assessment in feature level. Firstly, the corners in source images and the fused image are separately detected based on Smallest Univalue Segment Assimilating Nucleus (SUSAN) algorithm. Then, a corner similarity measure based on overlapping rate is proposed to measure the fusion quality. The proposed method avoids focus detection in the assessment procedure, which make evaluation results more reliable. Experimental results demonstrate that the proposed measure is more consistent with subjective evaluation. Comparing with other objective metrics, two meta-measures including correct ranking (CR) and subjective relevance (R) give our proposed measure the highest scores, 0.8377 and 0.7384, respectively. The area under the ROC curve (AUC) gives our metric the second-best scores of 0.8428.

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Acknowledgements

The work was supported by Youth Growth Science and Technology Plan Project of Jilin Provincial Department of Science and Technology (NO.20210508039RQ), “Thirteenth Five-Year Plan” Scientific Research Planning Project of Education Department of Jilin Province (NO.JJKH20200678KJ, NO.JJKH20210752KJ, NO.JJKH20200677KJ), Fundamental Research Funds for the Central Universities, JLU(NO.93K172020K05), and National Natural Science Foundation of China (NO.61806024, NO.61876070, NO.61801190).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Yuncong Feng

  2. Artificial Intelligence Research Institute, Changchun University of Technology, Changchun, 130012, China

    Yuncong Feng

  3. Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun, 130012, China

    Yuncong Feng, Rui Guo, Xuanjing Shen & Xiaoli Zhang

  4. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Rui Guo, Xuanjing Shen & Xiaoli Zhang

  5. Department of Information Engineering, Hainan Vocational University of Science and Technology, 571126, Haikou, China

    Xiaoli Zhang

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  1. Yuncong Feng
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Correspondence to Xiaoli Zhang.

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Feng, Y., Guo, R., Shen, X. et al. A measure for the evaluation of multi-focus image fusion at feature level. Multimed Tools Appl 81, 18053–18071 (2022). https://doi.org/10.1007/s11042-022-11976-3

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