Multi-focus image fusion using Singular Value Decomposition in DCT domain

Amin-Naji, Mostafa; Ranjbar-Noiey, Pardis; Aghagolzadeh, Ali

doi:10.1109/iranianmvip.2017.8342367

Cited by 26 publications

(16 citation statements)

References 37 publications

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“…In this section, the experimental results of the all-in-focused images are presented and evaluated by comparing them with other prominent techniques such as light field software [10], SML [9], DCT-STD [5], DCT-VAR-CV [4], SML-WHV [16], Agarwala's method [17], DCT-Sharp-CV [18], DCT-CORR-CV [19], and DCT-SVD-CV [20]. The all-in-focused images of different algorithms are shown in Figures 12-16.…”

Section: All-in-focused Image Combinationmentioning

confidence: 99%

All-in-Focused Image Combination in the Frequency Domain Using Light Field Images

Chantara

Jeon

2019

Applied Sciences

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All-in-focused image combination is a fusion technique used to acquire related data from a set of focused images at different depth levels, which suggests that one can determine objects in the foreground and background regions. When attempting to reconstruct an all-in-focused image, we need to identify in-focused regions from multiple input images captured with different focal lengths. This paper presents a new method to find and fuse the in-focused regions of the different focal stack images. After we apply the two-dimensional discrete cosine transform (DCT) to transform the focal stack images into the frequency domain, we utilize the sum of the updated modified Laplacian (SUML), enhancement of the SUML, and harmonic mean (HM) for calculating in-focused regions of the stack images. After fusing all the in-focused information, we transform the result back by using the inverse DCT. Hence, the out-focused parts are removed. Finally, we combine all the in-focused image regions and reconstruct the all-in-focused image.

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Section: All-in-focused Image Combinationmentioning

confidence: 99%

All-in-Focused Image Combination in the Frequency Domain Using Light Field Images

Chantara

Jeon

2019

Applied Sciences

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“…However, these methods suffer from quality loss and blocking artifacts due to the abundant errors generated. Thus, Amin-Naji et al [21] introduced a new multi-focus image fusion algorithm which utilized the Singular Value Decomposition (SVD) for multi-focus image fusion in the DCT domain. Although existing DWT-based and DCT-based image fusion methods are successfully for gray-scale image fusion, these methods cannot process RGB channels of color image holistically.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion Using Geometric Algebra Based Discrete Fourier Transform

Jiang

2020

IEEE Access

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For color image fusion, existing traditional algorithms regard each color image pixel as a scalar which may lose the relationships among spectral channels. In this paper, a novel multi-focus image fusion algorithm using geometric algebra based Discrete Fourier Transform (GA-DFT) is proposed. First, a novel Discrete Fourier Transform based on geometric algebra (GA) is provided. Second, the proposed algorithm represents the color image as a GA multivector by mapping the RGB channels into the GA space. Third, the variance is selected as a contrast measure to fuse the source images in GA space. The experimental results demonstrate that the proposed algorithm can achieve the comparable performance with its counterparts in terms of subjective quality evaluation, and outperform most of the traditional algorithms in terms of objective quality metrics, indicating good performance in image fusion applications.

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“…For the detail layer fusion, the multiresolution singular value decomposition (MSVD) method is used [37]. In the past, most methods have opted to apply singular value decomposition (SVD) method for image fusion tasks [38], [39], which differs from MSVD. Specifically, in Ref [38], SVD is used for base layer fusion by first decomposing and reconstructing the base layer, and then summing up all the base layers.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, in Ref [38], SVD is used for base layer fusion by first decomposing and reconstructing the base layer, and then summing up all the base layers. The fusion method in Ref [39] employs a DCT dictionary learning method. The singular value of the image is used as a reference for the coefficients fused in the DCT dictionary.…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Image Fusion Based on Visual Saliency Weight Map Combined With CNN

et al. 2020

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Convolutional neural networks (CNN) with their deep feature extraction capability have recently been applied in numerous image fusion tasks. However, the image fusion of infrared and visible images leads to loss of fine details and degradation of contrast in the fused image. This deterioration in the image is associated with the conventional ''averaging'' rule for base layer fusion and relatively large feature extraction by CNN. To overcome these problems, an effective fusion framework based on visual saliency weight map (VSWM) combined with CNN is proposed. The proposed framework first employs VSWM method to improve the contrast of an image under consideration. Next, the fine details in the image are preserved by applying multi-resolution singular value decomposition (MSVD) before further processing by CNN. The promising experimental results show that the proposed method outperforms state-of-the-art methods by scoring the highest over different evaluation metrics such as Q 0 , multiscale structural similarity (MS_SSIM), and the sum of correlations of differences (SCD). INDEX TERMS Convolutional neural network, image fusion, visual saliency weight map, multi-resolution singular value decomposition.

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Multi-focus image fusion using Singular Value Decomposition in DCT domain

Cited by 26 publications

References 37 publications

All-in-Focused Image Combination in the Frequency Domain Using Light Field Images

All-in-Focused Image Combination in the Frequency Domain Using Light Field Images

Multi-Focus Image Fusion Using Geometric Algebra Based Discrete Fourier Transform

Improving the Performance of Image Fusion Based on Visual Saliency Weight Map Combined With CNN

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