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Multi-Focus Image Fusion in DCT Domain

version 1.0.0 (1 MB) by Mostafa Amin-Naji
Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks Matlab


Updated 15 Dec 2019

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The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks (VSN). So the most of the researchers are interested in focus measurements calculation and fusion processes directly in DCT domain. Accordingly, many researchers developed some techniques which are substituting the spatial domain fusion process with DCT domain fusion process. Previous works in DCT domain have some shortcomings in selection of suitable divided blocks according to their criterion for focus measurement. In this paper, calculation of two powerful focus measurements, energy of Laplacian (EOL) and variance of Laplacian (VOL), are proposed directly in DCT domain. In addition, two other new focus measurements which work by measuring correlation coefficient between source blocks and artificial blurred blocks are developed completely in DCT domain. However, a new consistency verification method is introduced as a post-processing, improving the quality of fused image significantly. These proposed methods reduce the drawbacks significantly due to unsuitable block selection. The output images quality of our proposed methods is demonstrated by comparing the results of proposed algorithms with the previous algorithms.

Cite As

M. Amin-Naji and A. Aghagolzadeh, “Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks,” Journal of AI and Data Mining, Vol.6, No.2, pp. 233-250, 2018.

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MATLAB Release Compatibility
Created with R2019b
Compatible with any release
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