Novel PCA based pixel-level multi-focus image fusion algorithm

Jing, Hongyuan; Vladimirova, Tanya

doi:10.1109/ahs.2014.6880169

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…In Fig 7(e) and 7(f) , RMLP is compared with six state of the art methods that include pulse coupled neural network (PCNN) [ 1 ], nonsubsampled contourlet transform (NSCT) [ 21 ], interactive digital photomontage (IDP) [ 32 ], Laplacian pyramid (LP) [ 11 ], the contrast pyramid (CP) [ 16 ], Discrete Wavelet Transform (DWT) [ 46 ], the Shift-invariant Discrete Wavelet Transform (SIDWT) [ 25 ], Principal Component Analysis(PCA) [ 24 ] and sparse reconstruction method [ 29 ]. PCNN is implemented according to the descriptions in [ 1 ].…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Unlike transform domain based ones, the spatial domain based methods refer to the strategies that the source images are fused directly in gray space. These algorithms include weighted average fusion, weighted linear fusion, Principal Component Analysis (PCA) [ 24 ], Neutral Network, etc. The spatial domain based multi-focus image fusion can also be divided into two categories: pixel based and block based methods.…”

Section: Introductionmentioning

confidence: 99%

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A multi-focus image fusion method via region mosaicking on Laplacian pyramids

Liang

Zhang

et al. 2018

PLoS ONE

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In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-focus image fusion method via region mosaicking on Laplacian pyramids

Liang

Zhang

et al. 2018

PLoS ONE

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“…Strategies like these show increased benefits in spatial and spectral quality of the merged image when compared with different spatial types of fusion. [9] …”

Section: Transform Domain Fusionmentioning

confidence: 99%

“…In order to evaluate the caliber of the fused images, three quantitive indicators such as for example Gradients, RMSE, Correlation Coefficients were defined in the paper. Hongyuan Jing (2014) [9] proposed a brand new PCA based versatile picture mix algorithm to blend multi-focus photos with the same aesthetic perspective but different focus.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Comparative Study of Various Digital Image Fusion Techniques: A Review

Kaur¹,

Kaur²

2015

IJCA

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The key objective of vision fusion would be to merging information from multiple images of exactly the same view in order to deliver only the useful information. The PCA based ways of vision fusion are more suitable and time-saving in realtime systems using PCA based standards of still images. This paper has centered on the many image fusion techniques. The review has shown that the still much research is needed to improve the image fusion technique further. The IBLPCA based technique has shown quite improved results over the available techniques. This paper ultimately ends up with the suitable future directions.

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“…Multi-focus image fusion methods can be divided into two categories based on spatial and transform domain. In the spatial domain, the source images are fused directly in gray level such as image fusion using weighted average value and Principal Component Analysis (PCA) [8]. In the transform domain, images are fused by frequency domain using decomposition procedure.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Abas¹,

Baykan²

2021

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Focus is limited and singular in many image capture devices. Therefore, different focused objects at different distances are obtained in a single image taken. Image fusion can be defined as the acquisition of multiple focused objects in a single image by combining important information from two or more images into a single image. In this paper, a new multi-focus image fusion method based on Bat Algorithm (BA) is presented in a Multi-Scale Transform (MST) to overcome limitations of standard MST Transform. Firstly, a specific MST (Laplacian Pyramid or Curvelet Transform) is performed on the two source images to obtain their low-pass and high-pass bands. Secondly, optimization algorithms were used to find out optimal weights for coefficients in low-pass bands to improve the accuracy of the fusion image and finally the fused multi-focus image is reconstructed by the inverse MST. The experimental results are compared with different methods using reference and non-reference evaluation metrics to evaluate the performance of image fusion methods.

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Novel PCA based pixel-level multi-focus image fusion algorithm

Cited by 9 publications

References 15 publications

A multi-focus image fusion method via region mosaicking on Laplacian pyramids

A multi-focus image fusion method via region mosaicking on Laplacian pyramids

A Comparative Study of Various Digital Image Fusion Techniques: A Review

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

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