Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain

Zhang, Baohua; Zhang, Chuanting; Liu, Yuanyuan; Wu, Jianshuai; He, Lifeng

doi:10.1016/j.ijleo.2013.07.002

Cited by 28 publications

(7 citation statements)

References 19 publications

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“…The definition is given as:

in which:

where w A ( i , j ) and w B ( i , j ) are the corresponding gradient strengths for images A and B, respectively.

and

are the Sobel edge strength and orientation preservation values at location ( i , j ) for each source image [ 48 ]. The larger the value, the better the fusion effect result.…”

Section: The Experimental Results and Analysismentioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

Yang

Song

Huang

et al. 2014

Sensors

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This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

show abstract

“…The definition is given as:

in which:

where w A ( i , j ) and w B ( i , j ) are the corresponding gradient strengths for images A and B, respectively.

and

are the Sobel edge strength and orientation preservation values at location ( i , j ) for each source image [ 48 ]. The larger the value, the better the fusion effect result.…”

Section: The Experimental Results and Analysismentioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

Yang

Song

Huang

et al. 2014

Sensors

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show abstract

“…In [85], Zhang et al propose a fusion method based on PCNN in surfacelet transform domain. They use diferent PCNN models to fuse low-frenquency coefficients and high-frenquency coefficients, respectively.…”

Section: Other X-let Transformsmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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Recently, many researchers have paid their more attention to image fusion technique based on pulse coupled neural network (PCNN). In order to make the researchers to rapidly understand the research development of image fusion based on PCNN, it is systematically reviewed in the paper. On the basis of statistical analysis on published papers, firstly, PCNN and some modified models are introduced. Then we review the PCNN's applications in the field of image fusion. Subsequently, some existing problems are summarized, while we give some suggestions for the future research.

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“…The derivation of an image that comprises all relevant objects in focus became impossible due to the restricted depth of focus of optical lenses in CCD devices [1]. The solution to this problem is multi-focus image fusion, which combines multiple images of the same scene into a composite image which is more feasible for visualization and detection [2].The multi-focus image fusion methods are spatial and transform domain methods [3].Transform domain algorithms namely the multi-resolution algorithms, are more robust since the human visual system deals with information in a multi-resolution way, which is in line with the processing principle of transform domain algorithms.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform Based Multifocus Image Fusion Using Fusion rules

Radha¹,

Babu²

2019

IJECE

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<p>In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures. </p>

show abstract

Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain

Cited by 28 publications

References 19 publications

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

Review of Image Fusion Based on Pulse-Coupled Neural Network

Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform Based Multifocus Image Fusion Using Fusion rules

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