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1

The automatic focus segmentation of multi-focus image fusion

Hawari K., Ismail

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 1

art. no. e140352

EN

Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method is not sufficient to handle the ghost effect and unpredicted noises. Computational intelligence has developed quickly over recent decades, followed by the rapid development of multi-focus image fusion. The proposed method is multi-focus image fusion based on an automatic encoder-decoder algorithm. It uses deeplabV3+ architecture. During the training process, it uses a multi-focus dataset and ground truth. Then, the model of the network is constructed through the training process. This model was adopted in the testing process of sets to predict the focus map. The testing process is semantic focus processing. Lastly, the fusion process involves a focus map and multi-focus images to configure the fused image. The results show that the fused images do not contain any ghost effects or any unpredicted tiny objects. The assessment metric of the proposed method uses two aspects. The first is the accuracy of predicting a focus map, the second is an objective assessment of the fused image such as mutual information, SSIM, and PSNR indexes. They show a high score of precision and recall. In addition, the indexes of SSIM, PSNR, and mutual information are high. The proposed method also has more stable performance compared with other methods. Finally, the Resnet50 model algorithm in multi-focus image fusion can handle the ghost effect problem well.

2

Quantum-inspired particle swarm optimization algorithm with performance evaluation of fused images

Le Z, Xinman Z., Xuebin X, Dong W., Jie L., Yang L.

Optica Applicata

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2013

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Vol. 43, nr 4

679--691

EN

In order to improve and accelerate the speed of image integration, an optimal and intelligent method for multi-focus image fusion is presented in this paper. Based on particle swarm optimization and quantum theory, quantum particle swarm optimization (QPSO) intelligent search strategy is introduced in salience analysis of a contrast visual masking system, combined with the segmentation technique. The superiority of QPSO is quantum parallelism. It has stronger search ability and quicker convergence speed. When compared with other classical or novel fusion methods, several metrics for image definition are exploited to evaluate the performance of all the adopted methods objectively. Experiments are performed on both artificial multi-focus images and digital camera multi-focus images. The results show that QPSO algorithm is more efficient than non-subsampled contourlet transform, genetic algorithm, binary particle swarm optimization, etc. The simulation results demonstrate that QPSO is a satisfying image fusion method with high accuracy and high speed.

3

An application of swarm intelligence binary particle swarm optimization (BPSO) algorithm to multi-focus image fusion

Zhang X., Sun L., Han J., Chen G.

Optica Applicata

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2010

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Vol. 40, nr 4

949--964

EN

In this paper, an optimal and intelligent multi-focus image fusion algorithm is presented, expected to achieve perfect reconstruction or optimal fusion of multi-focus images with high speed. A synergistic combination of segmentation techniques and binary particle swarm optimization (BPSO) intelligent search strategies is employed in salience analysis of contrast feature-vision system. Also, several evaluations concerning image definition are exploited and used to evaluate the performance of the method proposed. Experiments are performed on a large number of images and the results show that the BPSO algorithm is much faster than the traditional genetic algorithm. The method proposed is also compared with some classical or new fusion methods, such as discrete wavelet-based transform (DWT), nonsubsampled contourlet transform (NSCT), NSCT-PCNN (pulse coupled neural networks (PCNN) method in NSCT domain) and curvelet transform. The simulation results with high accuracy and high speed prove the superiority and effectiveness of the present method.

4

Restoration and fusion optimization scheme of multifocus image using genetic search strategies

Zhang X., Han J., Liu P.

Optica Applicata

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2005

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Vol. 35, nr 4

927-942

EN

A novel and optimal algorithm is presented that is suitable for multifocus image fusion. A synergistic combination of segmentation techniques and genetic search strategies is employed in salience analysis of contrast feature-vision system. Some evaluation measures are suggested and applied to compare the performance of different fusion schemes. Two cases of the generated test images are discussed and extensive experiments demonstrate that in one case most fused images achieve reconstruction or optimized effects with respect to the reference image when the focus objectives are not overlapped blurred, and in the other case this method produces better results outperforming other conventional methods when the focus objectives are overlapped blurred. It is therefore shown that the performance of the fusion algorithm proposed optimizes further the fused image globally accomplishing absolute restoration or optimized fusion of multifocus image to the reference image. This algorithm is also suitable for the digital camera images of real scene and gets to be optimized well. s. 927-942, bibliogr. 21 poz..