Integration of Heuristic-Based Weighted Feature Fusion Concept for Multi-Focus Image Fusion Model

Background of the study: In particular, images only focus on a portion of the scene and only capture particular details because of the limited depth of the image-capturing device. Methods: An effective Multi-Focus Image Fusion (MFIF) model is designed. The MF images are gathered from benchmark sources. The collected images are given into Discrete Wavelet Transform (DWT) for dividing the images into high-frequency sub-bands as well as low-frequency sub-bands. Further, the weighted feature fusion is performed, whereas the weight optimization takes place using Grey Wolf Optimization (GWO). Further, the IDWT on the fused images helps to attain final fused multi-focus image. Results achieved: By considering the BRISQUE analysis, the developed model outperforms 11.11%, 57%, 12.2%, and 17.4% enhanced than RTH-DWT, RPOA-DWT, KOA-DWT, and GRO-DWT in terms of best analysis. Concluding remarks: Thus, the experimental analysis of the developed model has shown effective and superior performance.