Optimized dual-tree complex wavelet transform aided multimodal image fusion with adaptive weighted average fusion strategy.

Image fusion is generally utilized for retrieving significant data from a set of input images to provide useful informative data. Image fusion enhances the applicability and quality of data. Hence, the analysis of multimodal image fusion is a new to the research topic, which is designed by combining the images of multimodal into single image in order to preserveexact details. On the other hand, the existing approaches face challenges in the precise interpretation of source images, and also it have only captured local information without considering the wide range of information. To consider these weaknesses, a multimodal image fusion model is planned to develop according to the multi-resolution transform along with the optimization strategy. At first, the images are effectively analyzed from standard public datasets and further, the images given into the Optimized Dual-Tree Complex Wavelet Transform (ODTCWT) to acquire low frequency and high frequency coefficients. Here, certain parameters in DTCWT get tuned with the hybridized heuristic strategy with the Probability of Fitness-based Honey Badger Squirrel Search Optimization (PF-HBSSO) to enhance the decomposition quality. Then, the fusion of high-frequency coefficients is performed using adaptive weighted average fusion technique, whereas the weights are optimized using PF-HBSSOto achieve the optimal fused results. Similarly, the low-frequency coefficients are combined by average fusion. Finally, the fused images undergo image reconstruction using the inverse ODTCWT. The experimental evaluation of the designed multimodal image fusion illustratessuperioritythat distinguishes this work from others.