© The Institution of Engineering and Technology
Image fusion is becoming a promising technique for obtaining a more informative image by combining various source images captured by multimodal imaging systems. The technique finds application in several fields, such as medical imaging, material analysis, satellite imaging, including defence and civilian sectors. This study presents a model, named holoentropywhale fusion (HWFusion), for the image fusion. Two different multimodal images from magnetic resonance imaging (T1, T1C, T2, FLAIR) are fed into the wavelet transform to convert the images into four subbands. The wavelet coefficients are then fused using a weighted coefficient that utilises two factors, entropy and whale fusion factor, which are calculated using holoentropy and the proposed SPWhale optimiser, respectively. SPWhale is an algorithm designed by modifying whale optimisation algorithm with selfadaptive learning particle swarm optimisation and is used for the optimal selection of whale fusion factor. Inverse wavelet transform converts the fused wavelet coefficients obtained by the averaging of fusion factors into fused image. In a comparative analysis, the performance of HWFusion is compared with that of four existing techniques using, mutual information, peak signaltonoise ratio, and root meansquared error (RMSE), where it could attain mutual information of 1.8015, RMSE of 1.1701, and peak signaltonoise ratio of 40.6575.
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