Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation

Duan, Junwei; Chen, Long; Chen, C.L. Philip

doi:10.1016/j.neucom.2018.08.024

Cited by 23 publications

(5 citation statements)

References 37 publications

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“…where I and M are the original image and blurred image respectively. W is a point detection filter defined as (7). The evaluate strategy is based on the idea that image quality of focus areas is degraded significantly more than that of blurred areas when an image is digitally blurred.…”

Section: A the Clarity Measurement With Point Detection Filtermentioning

confidence: 99%

“…During the past two decades, a great variety of multi-focus image fusion algorithms have been proposed and these algorithms can be roughly divided into two categories: transform domain algorithms and spatial domain algorithms [7]. Transform domain algorithms share a 'decomposition-fusionreconstruction' framework.…”

Section: Introductionmentioning

confidence: 99%

“…To mitigate these artifacts, many region based image fusion methods have been presented. The region based approaches first segment the source images into regions rather than blocks by using image segmentation techniques such as normalized cut [20], watershed transform [21], and enhance linear spectral clustering [7]. Then the divided regions with different source images are fused according to their clarity, such as SF, energy of Laplacian of the image (EOL) [22], sum-modified-Laplacian (SML) [23] and FSWM [24].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

Chen

Yang

et al. 2020

IEEE Access

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An accurate and efficient measurement of pixel's sharpness is a critical factor in most multi-focus image fusion methods. In our practice, we found that the focused regions get more blurred than the defocus regions when the multi-focus images are blurred digitally. Based on this observation, a novel multi-focus image fusion method is presented in this paper. In the given fusion scheme, focused regions detection is achieved by point detection filter and Gaussian filter, which has been certified more effective than other frequently used image clarity measures. Moreover, unlike the other commonly used consistency verification, we propose a superpixel-based consistency verification (SCV) method by integrating the image superpixels to improve the fusion performance. Image superpixels can perceptually represent meaningful image local features. Two datasets of multi-focus images are used to conduct experiments. Experimental results demonstrate that the proposed method can be competitive with or even outperform the state-of-the-art fusion methods in terms of both subjective visual perception and objective evaluation metrics.

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Section: A the Clarity Measurement With Point Detection Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

Chen

Yang

et al. 2020

IEEE Access

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“…Vishal and Vinay ( 2018 ) proposed a block-based spatial domain multi-focus image fusion method, and used spatial frequency to measure the focus level of the blocks. Duan et al ( 2018 ) proposed a segmentation scheme based on enhanced LSC, which embeds the depth information of pixels in the clustering algorithm for multi-focus image fusion. The main advantage of fusion methods based on spatial domain lies in the fact that simple to implement, it can obtain the focus measure with low computational complexity.…”

Section: Introductionmentioning

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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“…For instance, multi-modal image fusion [3] tries to fuse images which have been acquired via different sensor modalities exhibiting diverse characteristics for a more reliable and accurate medical diagnosis. Another typical application is the multi-focus image fusion [4]. As the depth-of-field (DoF) of bright-field microscopy is only about 1˜2 micrometers, while the specimen's profile covers a much larger range and then the parts of the specimen that lie outside the object plane are blurred.…”

Section: Introductionmentioning

confidence: 99%

Scale-invariant structure saliency selection for fast image fusion

et al. 2019

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In this paper, we present a fast yet effective method for pixel-level scale-invariant image fusion in spatial domain based on the scale-space theory. Specifically, we propose a scale-invariant structure saliency selection scheme based on the difference-of-Gaussian (DoG) pyramid of images to build the weights or activity map. Due to the scale-invariant structure saliency selection, our method can keep both details of small size objects and the integrity information of large size objects in images. In addition, our method is very efficient since there are no complex operation involved and easy to be implemented and therefore can be used for fast high resolution images fusion. Experimental results demonstrate the proposed method yields competitive or even better results comparing to state-of-the-art image fusion methods both in terms of visual quality and objective evaluation metrics. Furthermore, the proposed method is very fast and can be used to fuse the high resolution images in real-time. Code is available at https://github.com/yiqingmy/Fusion.

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Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation

Cited by 23 publications

References 37 publications

Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

Multi-Focus Image Fusion With Point Detection Filter and Superpixel-Based Consistency Verification

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

Scale-invariant structure saliency selection for fast image fusion

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