IMAGE FUSION BASED ON MULTI-OBJECTIVE OPTIMIZATION

2014 ◽  
Vol 12 (02) ◽  
pp. 1450017 ◽  
Author(s):  
QIWEI XIE ◽  
QIAN LONG ◽  
SEIICHI MITA ◽  
CHUNZHAO GUO ◽  
AN JIANG
Keyword(s):  
Image Fusion ◽  
Energy Function ◽  
Spatial Information ◽  
Data Fitting ◽  
The Other ◽  
Spectral Information ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Gradient Representation ◽  
Key Parameter

This paper solves the image fusion problem by using a multi-object optimization strategy and a key energy function. The energy function mainly consists of two components. One ensures injection of more correlated detailed spatial information. The detailed information is extracted from gradient representation of the image to be fused. The other one guarantees that the spectral information is preserved by a data fitting term. By minimizing the proposed energy function, the fusion result can be obtained. Moreover, a key parameter is used in the energy function to adjust the weights of the spectral and spatial information during the image fusion. In this paper, a multi-object optimization is constructed to determine such a key parameter. The image fusion performance is evaluated through visional perception and some fusion indexes. Experimental results further demonstrate advantages of the proposed technique over the conventional fusion techniques.

Image Fusion Based on Kernel Estimation and Data Envelopment Analysis

2019 ◽  
Vol 18 (02) ◽  
pp. 487-515 ◽  
Author(s):  
Qiwei Xie ◽  
Xi Chen ◽  
Lin Li ◽  
Kaifeng Rao ◽  
Luo Tao ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Image Quality ◽  
Image Fusion ◽  
Energy Function ◽  
Spatial Information ◽  
Kernel Estimation ◽  
Data Envelopment ◽  
Panchromatic Image ◽  
Objective Parameter ◽  
Integrate State

This paper reports the improvement of the image quality during the fusion of remote sensing images by minimizing a novel energy function. First, by introducing a gradient constraint term in the energy function, the spatial information of the panchromatic image is transferred to the fused results. Second, the spectral information of the multispectral image is preserved by importing a kernel function to the data fitting term in the energy function. Finally, an objective parameter selection method based on data envelopment analysis (DEA) is proposed to integrate state-of-the-art image quality metrics. Visual perception measurement and selected fusion metrics are employed to evaluate the fusion performance. Experimental results show that the proposed method outperforms other established image fusion techniques.

scholarly journals Remote Sensing Image Fusion Algorithm Based on Two-Stream Fusion Network and Residual Channel Attention Mechanism

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Mengxing Huang ◽  
Shi Liu ◽  
Zhenfeng Li ◽  
Siling Feng ◽  
Di Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Attention Mechanism ◽  
Spectral Information ◽  
Spectral Features ◽  
Fusion Algorithm ◽  
Spatial Features ◽  
Remote Sensing Image Fusion

A two-stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two-channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen-2 and WorldView-2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

scholarly journals Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

2020 ◽  
Vol 12 (6) ◽  
pp. 1009
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Qimin Cheng ◽  
Xiaoyi Long ◽  
Yuxin Yuan
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Spatial Information ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Remote Sensing Image Fusion ◽  
Fusion Methods ◽  
Invariant Regions ◽  
The Difference

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.

A Multi-objective Optimization Strategy for Controlling the Structural Properties of Lightweight Mutiphasic PE/EVA Foams

2020 ◽  
pp. 597-601
Author(s):  
Sirwan Ghavami ◽  
Mohammad-Hasan Khademi ◽  
Farkhondeh Hemmati ◽  
Ali Fazeli ◽  
Jamshid Mohammadi-Roshandeh
Keyword(s):  
Structural Properties ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective

Dissociation of zinc phthalocyanine aggregation on bacterial surface is key for photodynamic antimicrobial effect

2018 ◽  
Vol 22 (09n10) ◽  
pp. 925-934 ◽  
Author(s):  
Dafeng Liu ◽  
Linsen Li ◽  
Jincan Chen ◽  
Zhuo Chen ◽  
Longguang Jiang ◽  
...  
Keyword(s):  
Antimicrobial Effect ◽  
Antibacterial Activities ◽  
Surface Hydrophobicity ◽  
The Other ◽  
Zinc Phthalocyanine ◽  
Bacterial Surface ◽  
E Coli ◽  
Bacterial Signaling ◽  
Bacterial Uptake ◽  
Key Parameter

Antimicrobial photodynamic therapy (aPDT) is an effective mean for killing bacteria in this era of increasing multi-antibiotic resistance, and possesses a number of unique advantages. Much effort has been devoted to the development a key component of aPDT photosensitizers (PSs). We synthesized a series of PSs with different positive charges (ZnPc(Lys)[Formula: see text], where [Formula: see text] 3, 5, 7, and studied their antibacterial activities and mechanisms against Escherichia coli (E. coli). Interestingly, the ZnPc(Lys)[Formula: see text] derivative showed stronger antibacterial effect (MIC = 25.3 [Formula: see text]M) than the other two PSs (MICs = 50.6 [Formula: see text]M), even though this PS did not have the highest uptake on bacteria among these PSs. It was ZnPc(Lys)[Formula: see text] that possessed the highest bacterial uptake. ZnPc(Lys)[Formula: see text] was found to have the highest monomeric fractions (62.0%) on bacteria surface than the other two PSs (37.9% for [Formula: see text] 3 and 33.9% [Formula: see text] = 7). These results clearly demonstrate that PS conformation on bacterial surface as a key parameter determining antibacterial efficacy of PSs. Other mechanistic aspects of photodynamic effects, including PS binding kinetics, bacterial surface hydrophobicity, zeta potential of bacteria, membrane permeability and bacterial signaling pathways were also studied.

A Multi-Objective Optimization Strategy With Priority Ranking of the Design Objectives

1990 ◽  
Author(s):  
Masahide Matsumoto ◽  
Jumpei Abe ◽  
Masataka Yoshimura
Keyword(s):  
Structural Design ◽  
Torsional Rigidity ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Response Level ◽  
Multi Objective ◽  
Lower Priority ◽  
Priority Ranking ◽  
Priority Order ◽  
Motorcycle Frame

Abstract Generally, two types of priorities are considered among multiple objectives in the design of machine structures. One of these objectives is named the “hard objective”, which is the absolutely indispensable design requirement. The other is called the “soft objective”, which has lower priority order. This paper proposes a multi-objective structural optimization strategy with priority ranking of those design objectives. Further, this strategy is demonstrated on the actual example of a motorcycle frame structural design which has three design objectives, (1) an increase in static torsional rigidity, (2) a reduction of dynamic response level, and (3) a decrease in the weight of the motorcycle frame.

scholarly journals Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

2020 ◽  
Author(s):  
Tomohiro Harada ◽  
Misaki Kaidan ◽  
Ruck Thawonmas
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computing Time ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evaluation Time ◽  
Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

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