Infrared and visual image fusion based on multi-scale feature decomposition

Yan, Huibin; Li, Zhongmin

doi:10.1016/j.ijleo.2019.163900

Cited by 28 publications

(7 citation statements)

References 42 publications

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“…The proposed BSO-TS-RLS and BSO-MS-RLS algorithms for bilinear state-space systems in this paper can combine the iterative methods (Ding, Pan, Alsaedi, & Hayat, 2019;Xu, 2015Xu, , 2016Xu, Chen, & Xiong, 2015;Xu, Ding, & Zhu, 2019), the recursive methods (Xu & Ding, 2017a;Xu, Ding, Gu, Alsaedi, & Hayat, 2017;Xu, Xiong, Alsaedi, & Hayat, 2018;Zhang, Ding, Xu, Alsaedi, & Hayat, 2019;Zhang, Ding, Xu, & Yang, 2019) and the statistical tools (Tang et al, 2019;Xu, 2014Xu, , 2017 to study the parameter estimation of linear and nonlinear systems with coloured noise (Ding, Chen, Lin, & Wan, 2019) and can be applied to other fields (Cao, Wang, Liu, Li, & Xie, 2019;Liu et al, 2019;Yan & Li, 2019).…”

Section: Discussionmentioning

confidence: 99%

Hierarchical parameter and state estimation for bilinear systems

Zhang

Ding

2020

International Journal of Systems Science

163

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The identification of state-space models of bilinear systems is studied in this paper. The parameters to be identified of the considered system are coupled with the unknown states, which makes the identification problem more difficult than that of the linear state-space model. For the coupled variables, this paper introduces the interaction estimation theory to study an on-line algorithm for joint state-parameter estimation. To be more specific, a state observer in the bilinear form is established by minimising the state estimation error covariance matrix in the same way of a Kalman filter on the condition that the parameters are known. Then, a bilinear state observer based recursive least squares algorithm is developed using the least squares method. Moreover, for the purpose of improving the computational efficiency, a bilinear state observer based two-stage recursive least squares algorithm and a bilinear state observer based multi-stage recursive least squares algorithm are proposed by decomposing the system into several subsystems based on the hierarchical identification. Finally, a numerical simulation is employed to show the specific performance of the proposed approaches.

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Section: Discussionmentioning

confidence: 99%

Hierarchical parameter and state estimation for bilinear systems

Zhang

Ding

2020

International Journal of Systems Science

163

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“…There are still some interesting topics that are worth discussing. The proposed methods proposed in this paper can be used for modeling and prediction [61][62][63][64][65] and can be extended to study the parameter estimation problems of different systems with colored noises [66][67][68][69][70] and can be applied to other literatures [71][72][73][74][75] such as information and networked communication systems. [76][77][78][79][80][81][82]…”

Section: Discussionmentioning

confidence: 99%

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Liu

Ding

Hayat

2020

Intl J Robust & Nonlinear

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Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.

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“…Kong et al [17] proposed a novel infrared and visible image fusion method called the structure transferring fusion (STF) method. Yan and Li [18] proposed a novel IR and VI image fusion method based on multiscale feature decomposition (MSFD). In sparse representation-based infrared and visible image fusion, Zhu et al [19] proposed a novel image fusion scheme based on image cartoon-texture decomposition and sparse representation to preserve the structure information perform the detailed information of source images.…”

Section: Introductionmentioning

confidence: 99%

Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

et al. 2021

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Image fusion is a visual enhancement technique that combines source images from different sensors to produce a more robust and informative fused image for subsequent processing or decision making. Infrared and visible light images share complementary properties that enable the production of robust and informative fused images. This paper proposed an infrared and visible image fusion method that improved the tetrolet framework to improve infrared and visible image fusion quality. First, the source image is enhanced by bicubic interpolation. The improved tetrolet transform then decomposes the enhanced source image; the high-frequency components are fused by convolutional sparse representation theory and combined with corresponding rules, and the low-frequency components are fused by defining ISER descriptors. Finally, we use the inverse transform to reconstruct the fused image. Qualitative and quantitative experimental results on five groups of typical infrared and visible image datasets demonstrate the proposed method's effectiveness. The proposed method exhibits better performances on subjective vision and objective indexes compared with the other state-of-the-art methods.INDEX TERMS Image fusion, improved tetrolet transform, convolutional sparse representation, ISER descriptor.

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Infrared and visual image fusion based on multi-scale feature decomposition

Cited by 28 publications

References 42 publications

Hierarchical parameter and state estimation for bilinear systems

Hierarchical parameter and state estimation for bilinear systems

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

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