A non-reference image fusion metric based on mutual information of image features
By Mohammad Haghighat, Ali Aghagolzadeh, Hadi Seyedarabi. Computers & electrical engineering, 2011
440 citations recorded by the source corpus.
The widespread usage of image fusion causes an increase in the importance of assessing the performance of different fusion algorithms. The problem of introducing a suitable quality measure for image fusion lies in the difficulty of defining an ideal fused image. In this paper, we propose a non-reference objective image fusion metric based on mutual information which calculates the amount of information conducted from the source images to the fused image. The considered information is represented by image features like gradients or edges, which are often in the form of two-dimensional signals. In this paper, a method of estimating the joint probability distribution from marginal distributions is also presented which is employed in calculation of mutual information. The proposed method is compared with the most popular existing algorithms. Various experiments, performed on several databases, certify the efficiency of our proposed method which is more consistent with the subjective criteria.
Subfields: Aerospace Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/5bb9988389faadd30b6ee3deb170741f926a3136
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Citations in the corpus
- SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “…all algorithms, except GTF [17], ANVF [3], CSMCA [39], SPD-MEF [41], and SFMD [42] that are Evaluation Metrics: Four metrics are selected to quantify the evaluation, including feature mutual information (FMI) [84], Q abf , structural similarity (SSIM) [75], and peak signal-to-noise ratio (PSNR).”
- A medical image fusion method based on convolutional neural networks (2017, background): “They are the information entropy (EN ) of the fused image, the feature mutual information (FMI) [29], Xydeas-Petrovic’s gradient based metric QG [30], Piella-Heijmans’s similarity based metric QE [31], and the visual information fidelity fusion (V IFF ) metric [32].”
- CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach (2024, methodology): “…six image quality metrics are utilized to assess the objective evaluation, which includes: Entropy (En) [65]; Standard Deviation (SD) [66]; Mutual Information (MI) [67]; Image feature based Mutual Information ( FMI dct , FMI pixel ) [68]; the sum of correlations of di ff erences (SCD) [69].”