Abstract
In the technologies, increasing attention is being paid to image fusion; nevertheless, how to objectively assess the quality of fused images and the performance of different fusion algorithms is of significance. In this paper, we propose a novel objective non-reference measure for evaluating image fusion. This metric employs the properties of Arimoto entropy, which is a generalization of Shannon entropy, measuring the amount of information that the fusion image contains about two input images. Preliminary experiments on multi-focus images and multi-modal images using the average fusion algorithm, contrast pyramid, principal component analysis, laplacian pyramid, guided filtering and discrete cosine transform have been implemented. In addition, a comparison has been conducted with other relevant quality metrics of image fusion such as mutual information, normalized mutual information, Tsallis divergence and the Petrovic measure. The experimental results illustrate that our presented metric correlates better with the subjective criteria of these fused images.
Highlights
Image fusion is a significant technology in the field of digital image processing
Two groups of tested images, in which the first group includes two pairs of multi-focus images and the second one consists of the multi-modal images, were exploited in order to evaluate our proposed metric
The aim of image fusion is to generate more informative images that are suitable for human visual perception; namely, the final fusion images obtained by different fusion methods must fit with the human visual system (HVS)
Summary
Image fusion is a significant technology in the field of digital image processing. The fusion between the different images accounts for the process of integrating two or multiple source images into a new image. Wang modeled any image distortion as a combination of three factors: luminance distortion, correlation losing and contrast distortion, and proposed a universal image quality index (UIQI) [3] This index did not exploit the human visual system, the experimental results demonstrate that it performs essentially better than other metrics. Han et al [12] presented a fusion metric based on a multi-resolution strategy, where visual information fidelity (VIF) was employed to evaluate the performance of the image fusion objectively. In their scheme, the input and output fused images were first filtered and separated into blocks. The presented metric is consistent with the subjective visual inspection
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