Multi-Focus Image Fusion Using Block-Wise Color-Principal Component Analysis

Siddique, Abubakar; Xiao, Bin; Li, Weisheng; Nawaz, Qamar; Hamid, Isma

doi:10.1109/icivc.2018.8492725

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…In spatial domain based fusion methods, fused images are computed by processing the pixels of the input multi-focus images. Some methods operate at the pixel level, e.g., [ 11 , 14 , 36 ]; some are block based, e.g., [ 37 , 38 , 39 ]; and others are region based methods, such as [ 40 , 41 , 42 ]. Usually, pixel-by-pixel averaging of source images does not achieve plausible results as it may introduce different artifacts in the fuse image [ 13 , 43 ].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Exploiting Superpixels for Multi-Focus Image Fusion

Ilyas

Farid

Khan

et al. 2021

Entropy

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Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Exploiting Superpixels for Multi-Focus Image Fusion

Ilyas

Farid

Khan

et al. 2021

Entropy

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show abstract

“…BT is different sources that capture a simple technique for merging the information. Brovey is also called the color normalization transform (CNT) because it involves a red, green, blue (RGB) color transform approach [ 24 ]. Average and maximum/minimum selection is also spatial-domain method [ 25 ].…”

Section: Literature Studymentioning

confidence: 99%

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Khan¹,

Khan

Alharbi

et al. 2021

Journal of Healthcare Engineering

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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.

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“…However, this method introduced some noise when obtaining the fused image. In 2018, Siddique et al [25] proposed an image fusion method based on color-principal component analysis (C-PCA), which was divided into three stages: first, color PCA and enhanced color properties were used to generate the intermediate images; second, the salient features of an image were extracted by Laplacian of Gaussian; third, the spatial frequency was used as the focus measurement to obtain the final fused image. In 2020, Tyagi et al [26] proposed a hybrid and parallel processing fusion technique for multi-focus images based on stationary wavelet transform (SWT) and principal component analysis (PCA).…”

Section: Multi-focus Image Fusionmentioning

confidence: 99%

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

et al. 2020

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Multi-sensor image fusion is used to combine the complementary information of source images from the multiple sensors. Recently, conventional image fusion schemes based on signal processing techniques have been studied extensively, and machine learning-based techniques have been introduced into image fusion because of the prominent advantages. In this work, a new multi-sensor image fusion method based on the support vector machine and principal component analysis is proposed. First, the key features of the source images are extracted by combining the sliding window technique and five effective evaluation indicators. Second, a trained support vector machine model is used to extract the focus region and the non-focus region of the source images according to the extracted image features, the fusion decision is therefore obtained for each source image. Then, the consistency verification operation is used to absorb a single singular point in the decisions of the trained classifier. Finally, a novel method based on principal component analysis and the multi-scale sliding window is proposed to handle the disputed areas in the fusion decision pair. Experiments are performed to verify the performance of the new combined method.

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Multi-Focus Image Fusion Using Block-Wise Color-Principal Component Analysis

Cited by 6 publications

References 20 publications

Exploiting Superpixels for Multi-Focus Image Fusion

Exploiting Superpixels for Multi-Focus Image Fusion

Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

Multi-Sensor Image Fusion Using Optimized Support Vector Machine and Multiscale Weighted Principal Component Analysis

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