Abstract
In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset.
Highlights
In the field of image fusion, the subfield multifocus image fusion is one of the most significant and valuable approaches to handle the problem of defocusing that some parts of the image are not in focus and blurred due to the limited depth of focus in the optical lens of traditional cameras or large aperture and microscopes cameras
The experimentation is conducted on different multifocus image sets for the proposed hybrid methods. e proposed hybrid methods like discrete wavelet transform (DWT) + Laplacian Filter (LF), DWT + unsharp masking, DWT + (LF + DFT), stationary wavelet transform (SWT) + LF, SWT + unsharp masking, and SWT + (LF + DFT) are compared with the traditional methods such as average method, minimum method, DWT, and SWT methods. e algorithms are implemented, and the simulations are performed using the MATLAB 2016b application software tool. e resultant images are evaluated in two ways, i.e., quantitatively and qualitatively
mean absolute error (MAE) values are small for the proposed methods on both image sets, promising results. e large value of entropy expresses the good results; for the “Books” image set, the DWT technique has a large value, while the “Clock” image sets the proposed methods to demonstrate the impressive results. e CORR is a quantitative measure that demonstrates the correlation between the true image and the resultant image
Summary
In the field of image fusion, the subfield multifocus image fusion is one of the most significant and valuable approaches to handle the problem of defocusing that some parts of the image are not in focus and blurred due to the limited depth of focus in the optical lens of traditional cameras or large aperture and microscopes cameras. Various images of a similar scene but with different focus settings can be merged into a signal image (one image) with more information, where all the parts of the image are entirely focused. E practical technique of multifocus image fusion should need to accomplish the requirements that all the information of the focused regions in the source images is preserved in the resultant image [1]. Fusion has been introduced as a large number of techniques over the past couple of decades; some of them are very popular methods and achieve high accuracy, such as stationary wavelet transform (SWT) [7], discrete wavelet transform (DWT), dual-tree complex wavelet transform (DT-CWT), and discrete cosine transform (DCT) [2]. Most multifocus image fusion techniques are divided into four major classes [1, 8]. e first category is multiscale decomposition or frequency domain techniques such as wavelet transformation [8, 9], complex wavelet transformation [1, 10], nonsubsampled contourlet transform [11], DWT [2], and SWT [12]. e second category is sparse representation techniques like an adaptive SR model proposed in [13] for simultaneous image fusion and denoising and multitask sparse representation technique [14]. e third category of techniques is based on computational photography, such as light-field rendering [15]. is kind of technique finds more of the physical formation of multifocus images and reconstructs the all-in-focus images
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