Abstract
Multi-focus image fusion is a very essential method of obtaining an all focus image from multiple source images. The fused image eliminates the out of focus regions, and the resultant image contains sharp and focused regions. A novel multiscale image fusion system based on contrast enhancement, spatial gradient information and multiscale image matting is proposed to extract the focused region information from multiple source images. In the proposed image fusion approach, the multi-focus source images are firstly refined over an image enhancement algorithm so that the intensity distribution is enhanced for superior visualization. The edge detection method based on a spatial gradient is employed for obtaining the edge information from the contrast stretched images. This improved edge information is further utilized by a multiscale window technique to produce local and global activity maps. Furthermore, a trimap and decision maps are obtained based upon the information provided by these near and far focus activity maps. Finally, the fused image is achieved by using an enhanced decision maps and fusion rule. The proposed multiscale image matting (MSIM) makes full use of the spatial consistency and the correlation among source images and, therefore, obtains superior performance at object boundaries compared to region-based methods. The achievement of the proposed method is compared with some of the latest techniques by performing qualitative and quantitative evaluation.
Highlights
IntroductionOne of the most important objectives is obtaining a focused region of interest
During image acquisition, one of the most important objectives is obtaining a focused region of interest
To show the superiority of the proposed multiscale image matting (MSIM), a comparison was performed with discrete wavelet transform (DWT) [29], guided filtering based fusion (GFF) [13], discrete cosine transform (DCT) [30], dense sift (DSIFT) [20], multi-scale morphological focus-measure (MSMFM) [9], and convolutional neural network (CNN) [24] on a multifocus image dataset [31]
Summary
One of the most important objectives is obtaining a focused region of interest. Multi-focus image fusion (combine images with different focused objects) has received tremendous attention amongst the researchers. This fused image offers high quality containing more detailed information [1,2]. Several methods are developed to fuse multiple images, which are broadly grouped into transform and spatial domains [3,4]. Spatial domain methods are further classified into pixel [5,6] and region based methods [7,8]. The spatial domain methods form the fuse image by choosing the pixels/regions/blocks that are focused.
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