Image fusion method based on spatially masked convolutional sparse representation

Xing, Changda; Wang, Zhisheng; Ouyang, Quan; Dong, Chong; Duan, Chaowei

doi:10.1016/j.imavis.2019.08.010

Cited by 16 publications

(9 citation statements)

References 44 publications

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“…From the view-point of fusion aim, salient feature extraction method and fusion rule are the two core issues. Around the first task, a large number of image representation/analysis methods have been designed and utilized, such as various spatial models [5], multi-scale transform models [6], Sparse Representation (SR) and Convolutional SR (CSR) models [7][8][9][10][11], and the popular neural network models [4,12,13]. At the same time, many general or task-specific fusion schemes were also elaborated to measure the salient feature's activity degrees (or separate the focus/un-focus regions for MFIF) and compose them together with various complex optimization models [2,14].…”

Section: Related Work and Innovation Motivationmentioning

confidence: 99%

A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

Chen

Zhang

et al. 2021

IET Image Processing

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In this paper, a new Multi-Focus Image Fusion (MFIF) method based on multi-source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi-focus source images, a multi-source joint layering regularization model was designed to divide the sources into a common base-layer and respective focus detail-layers. This strategy can overcome the defects caused by source layering separately effectively. In detail-layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail-layer fusion rule combined the CSR coefficient maps pairwise with the window based select-max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors' method.

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Section: Related Work and Innovation Motivationmentioning

confidence: 99%

A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

Chen

Zhang

et al. 2021

IET Image Processing

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“…Li et al [20] used low-rank sparse decomposition to increase noise robustness during image fusion. Xing et al [21] proposed an image fusion method based on spatially masked convolutional sparse representation (SMCSR), which obtained better fusion results than traditional sparse representations. Li et al [22] proposed a multilevel image decomposition method based on latent low-rank representation (LatLRR) called MDLatLRR.…”

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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“…The sparse representation model utilizes the training samples to learn a complete dictionary to obtain the sparse representation of the signal. In addition, sparse representation has been widely used in image processing, such as image fusion [34], classification [35], object detection [36], etc. In particular, Wright et al [37,38] defined the problem of decomposing a given data matrix into the sum of a low-rank matrix and a sparse matrix as RPCA which can be described to solve Y=L+S with unknown L (low-rank matrix) and S (sparse matrix).…”

Section: Introductionmentioning

confidence: 99%

Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

Liu

Gao

Shichun

et al. 2020

Infrared Physics & Technology

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In this paper, we propose a structured iterative alternating sparse matrix decomposition to efficiently decompose the input multidimensional data from active thermography into the sum of a lowrank matrix, a sparse matrix, and a noise matrix. In particular, the sparse matrix is further factorized into a pattern constructed dictionary matrix and a coefficient matrix. The estimation of the dictionary matrix and coefficient matrix is based on integrating the vertex component analysis with the framework of the alternating direction method of multipliers. In addition, the joint structure sparsity and nonnegative constraint are emphasized as part of the learning strategy. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out by applying the proposed method to thermal imaging diagnostic system for carbon fiber reinforced plastics(CFRP) defects detections. The validation study has been conducted by comparing the proposed method with the current state-of-the-art algorithms. The results indicate that the proposed method significantly improves the contrast ratio between the defective regions and the non-defective regions.

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Image fusion method based on spatially masked convolutional sparse representation

Cited by 16 publications

References 44 publications

A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

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

Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

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