Abstract
Digital image acquisition devices suffer from a narrow depth of field (DoF) due to optical lenses installed in them. As a result, the generated images have varying focus, thereby losing essential details. Multi-focus image fusion aims to synthesize an all-in-focus image for better scene perception and processing. This paper proposes an effective region-based focus fusion approach based on a novel focus measure derived from multi-scale half gradients extracted from the morphological toggle-contrast operator. The energy of the same focus measure is combined with spatial frequency to design a composite focusing criterion (CFC) to roughly differentiate between the focussed and defocussed regions. The high-frequency information obtained is further enhanced using a guided filter, taking focus guidance from the source images. The best focus region is selected using a pixel-wise maximum rule which is further converted into a refined binarized decision map using a small region removal technique. At this point, the guided filter is re-utilized to verify the spatial correlation concerning the initial fused image to obtain the final decision map for final fusion. Experimental results exhibit the discussed algorithm’s efficacy over current fusion approaches in visual perception and quantitative metrics on registered and un-registered multi-focus datasets.
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Roy, M., Mukhopadhyay, S. (2022). Multi-focus Image Fusion Using Morphological Toggle-Gradient and Guided Filter. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_9
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