Multi-focus Image Fusion Based on Fuzzy and Wavelet Transform

Saeedi, Jamal; Faez, Karim; Mozaffari, Saeed

doi:10.1007/978-3-642-10268-4_113

Cited by 7 publications

(3 citation statements)

References 17 publications

(28 reference statements)

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“…The Six images (A)–(F) in Figure 6 are obtained from the Slavica dataset (Slavica, 2011 ). The one image (C) in Figure 4 and the two images (G) and (H) in Figure 6 are obtained from the Saeedi dataset (Saeedi and Faez, 2015 ). The one image (J) in Figure 6 is obtained from the Bavirisetti dataset ( Bavirisetti ).…”

Section: Resultsmentioning

confidence: 99%

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition

et al. 2021

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Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained. Second, the activity level and matching level are exploited in the focus decision mapping of the low-frequency sub-image fusion, with the former calculated by using local window energy and the latter measured by the color difference between color pixels expressed by a quaternion. Third, the fusion results of low-frequency coefficients are incorporated into the fusion of high-frequency sub-images, and a local contrast fusion rule based on the integration of high-frequency and low-frequency regions is proposed. Finally, the fused images are reconstructed employing inverse transform of the QMSVD. Simulation results show that image fusion using this method achieves great overall visual effects, with high resolution images, rich colors, and low information loss.

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Section: Resultsmentioning

confidence: 99%

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition

et al. 2021

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“…The spatial domain fusion includes averaging, spatial frequency [3], HIS transform [4] and so on. The transform domain fusion includes discrete wavelet [5], curvelet transform [6], Contourlet transform [7], Nonsubsampled contourlet transform [8] and so on.…”

Section: Introductionmentioning

confidence: 99%

Multi-focus Image Fusion Algorithm Based on Motivated Pulse Coupled Neural Networks Using Nonsubsampled Contourlet Transform

Bai¹,

Jiang²,

Huang³

et al. 2017

dtcse

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Abstract. In this paper, a multi-focus image fusion algorithm based on motivated pulse coupled neural networks using nonsubsampled contourlet transform (NSCT) is proposed. Firstly, NSCT is used to decompose the source image in multi scale and multi directions to obtain the low frequency coefficients and high frequency coefficients. Secondly, Fourier transform method is used for saliency detection. We choose the coefficients which have larger saliency for high frequency coefficients. In the process of choosing the low frequency, the spatial frequency is used as the input of the Pulse coupled neural network (PCNN) to obtain the firing times of the low frequency. If the firing times of two images are not the same, then we choose coefficients which have larger firing times. Otherwise, coefficients which have larger saliency will be chosen. Lastly, inverse transformation of NSCT is used to obtain the fused image. The proposed method is compared with four other common methods, and the experimental results show that the proposed method performs better on both subjective and objective evaluation indicators.

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“…And image fusion algorithms based on fuzzy logic have been studied during the last two decades. These algorithms consist of utilization of fuzzy rules to fuse two images in terms of gray level values [2][3][4][5][6][7][8] and the mergence of wavelet coefficients from these two images according to the extracted features [9][10][11][12][13]. And some neural networks have also been used to generate a fuzzy inference system structure automatically from the input image data obtained from different sensors [6,7,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Multisensor image fusion using fuzzy logic for surveillance systems

Zheng

2010

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery

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Multisensor image fusion has its effective utilization for surveillance. In this paper, we utilize a fuzzy logic approach to fuse images from different sensors, in order to enhance visualization for surveillance. With the help of fuzzy if-then rules and membership functions designed for the image data set, the fuzzy logic approach can model and combine the images to enhance the contrast of the fused image. Mamdani-type fuzzy inference system is used to combine the images. Subjective and objective image fusion performance measures are introduced to assess the performance of image fusion schemes. Experimental results show that the fuzzy logic approach is effective to fuse images from different sensors.

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Multi-focus Image Fusion Based on Fuzzy and Wavelet Transform

Cited by 7 publications

References 17 publications

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition

Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition

Multi-focus Image Fusion Algorithm Based on Motivated Pulse Coupled Neural Networks Using Nonsubsampled Contourlet Transform

Multisensor image fusion using fuzzy logic for surveillance systems

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