HoEnTOA: Holoentropy and Taylor Assisted Optimization based Novel Image Quality Enhancement Algorithm for Multi-Focus Image Fusion
In machine vision as well as image processing applications, multi-focus image fusion strategy carries a prominent exposure. Normally, image fusion is a method of merging of information extracted out of two or more than two source images fused to produce a solitary image, which is much more instructive as well as much suitable for computer processing and visual perception. In this research paper authors have devised a novel image quality enhancement algorithm by fusing multi-focus images, in short, termed as HoEnTOA. Initially, contourlet transform is incorporated to both of the input images for generation of four respective sub-bands of each of input image. After converting into sub-bands further holoentropy along with proposed HoEnTOA is introduced to fuse multi-focus images. Here, the developed HoEnTOA is integration of Taylor series with ASSCA. After fusion, the inverse contourlet transform is incorporated for obtaining last fused image. Thus, the proposed HoEnTOA effectively performs the image fusion and has demonstrated better performance utilizing the five metrics i.e. Root Mean Square Error with a minimum value of 3.687, highest universal quality index value of 0.984, maximum Peak Signal to Noise Ratio of 42.08dB, maximal structural similarity index measurement of 0.943, as well as maximum mutual information of 1.651.
Contourlet transform, Medical imaging, Optimization based fusion technique, Taylor series, Two level image fusion
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