Multi-objective optimization and gray association for multi-focus image fusion

Wang, Suli; Luo, Xiangfeng

doi:10.1177/1748301816640712

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…16 Grey relational analysis is a multi-factor statistical analysis method, which is based on the sample data of each factor and applies the grey relational degree to describe the weight and order of the relationships among various influential factors. [17][18][19] Hence the loss risk caused by information asymmetry can be reduced to a large extent. Moreover, there is no stringent requirement on the number of data samples or typical distribution rules, and the calculated amount is relatively small, which is suitable for using by mobile phone terminals.…”

Section: Introductionmentioning

confidence: 99%

A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

Xiao-lin

Gao

Pang

2021

Journal of Algorithms & Computational Technology

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In order to select and recommend the optimal parking lot for the users, a novel intelligent selection algorithm based on the grey relational analysis considering user preferences is proposed. The parking-lot evaluation index system consisting of indicators in effectiveness, convenience, economy, environmental attribute and agreeableness is firstly established. The comprehensive weight determination method combining the expert estimation method with the user preference settings is proposed to determine the comprehensive weight of each evaluation index, and this method can meet the personalized needs of individual user to the greatest extent. The evaluation indexes considering various representative objective and subjective influential factors including the driving distance between the current location and the target parking lot, the walking distance between the target parking lot and the destination, the remaining parking spaces, the parking fee, the condition of the hardware facilities and service quality are determined, and the corresponding comprehensive weight of each evaluation index is calculated. Then the parking lots near the destination are evaluated by the grey relational analysis in order to select the optimal one to the user for parking reservation. This algorithm has been proven to be effective by the APP on Android mobile phones.

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Section: Introductionmentioning

confidence: 99%

A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

Xiao-lin

Gao

Pang

2021

Journal of Algorithms & Computational Technology

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Genetic Algorithm Approach for Image Fusion: A Simple Method and Block Method

Kulkarni¹

2022

IJITEE

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The sensors available nowadays are not generating images of all objects in a scene with the same clarity at various distances. The progress in sensor technology improved the quality of images over recent years. However, the target data generated by a single image is limited. For merging information from multiple input images, image fusion is used. The basis of image fusion is on the image acquisition as well as on the level of processing and under this many image fusion techniques are available. Several input image acquisition techniques are available such as multisensor, multifocus, and multitemporal. Also, image fusion is performed in four different stages. These levels are the level of the signal, pixel level, level of feature, and level of decision-making. Further, the fusion methods are divided into two domains i.e spatial and frequency domains. The fusion in spatial domain images uses inputs directly to work on pixels, while the transition refers to frequency domain image fusion on input images before fusion. The limitation of spatial domain image fusion is spectral degradation. To overcome this limitation, the fusion of transform domain images is preferred which uses several transforms. The results generated by transform methods are superior to spatial domain methods. But there is a scope to improve the results or to find the optimized results. Optimization can be achieved by using evolutionary approaches. The evolutionary computation approach is an effective way of finding the required solution for a complex problem. An evolutionary algorithm is a guided random search used for optimization. The biological model of evolution and natural selection inspires it. The different types of evolutionary computing algorithms include Genetic algorithm, Genetic Programming, Evolutionary programming, Learning Classifier System, Ant Colony Optimization, Artificial Bee Colony Optimization, Particle Swarm Optimization, Evolution strategy, Swarm intelligence, Tabu Search, Cuckoo Search, etc. Three genetic algorithm-based image fusion techniques are proposed: a genetic algorithm with one population, a genetic algorithm with separate populations, and a block method. In the block method, an array of numbers in one chromosome is generated. The result obtained by the proposed techniques are compared with existing methods and observed that the results are improved. The graphical representation of performance parameters reflects that the block method is better.

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Image Fusion by a Hybrid Multiobjective Genetic Algorithm Technique

Kulkarni¹

2022

IJRTE

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Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g. panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however now not recognized whereas multispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the genetic algorithm. Comparing the results obtained by these methods, the output image by the genetic algorithm is clearer. The feature of the resultant image is verified through parameters such as root mean square error, peak signal to noise ratio, mutual information, and spatial frequency. In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a genetic algorithm is used for image fusion. This is again compared with genetic algorithm results. The same performance parameters are used. And it is observed that the hybrid genetic algorithm is superior to the genetic algorithm. Here the only root means square error parameter is considered under the fitness function of the genetic algorithm so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the genetic algorithm then all parameters using a hybrid genetic algorithm will give better performance. This method is called a hybrid multiobjective genetic algorithm.

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Multi-objective optimization and gray association for multi-focus image fusion

Cited by 3 publications

References 29 publications

A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

Genetic Algorithm Approach for Image Fusion: A Simple Method and Block Method

Image Fusion by a Hybrid Multiobjective Genetic Algorithm Technique

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