Infrared and visible images fusion based on RPCA and NSCT

Zhang, Fu; Wang, Xue; Xu, Jin; Zhou, Ning; Zhao, Yufei

doi:10.1016/j.infrared.2016.05.012

Cited by 95 publications

(26 citation statements)

References 20 publications

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“…It is well known that MST-based methods [11]- [16] are the most researched fusion methods. Their main process includes three steps, i.e., decomposition, fusion, and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A General Perceptual Infrared and Visible Image Fusion Framework Based on Linear Filter and Side Window Filtering Technology

Yan

2020

IEEE Access

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Recently, edge-preserving filters have achieved great success in infrared (IR) and visible (VI) image fusion field. However, most edge-preserving filters are complex. In this paper, with the side window filtering technology by which most filters can improve their edge-preserving capabilities, we propose a general perceptual IR and VI image fusion framework with simple linear filter. Firstly, the source images are decomposed into edge feature components, hybrid components and base components by using linear filter and its side window version. Then, these components are combined by max-absolute fusion rule and improved max-absolute fusion rule. Finally, the fused image is reconstructed by adding all the fused components. In our experiments, two popular linear filters, i.e., box filter and Gaussian filter, are used to verify the effectiveness of the proposed framework. Experimental results show that the proposed fusion framework can obtain better perceptual fusion results than compared methods. INDEX TERMS Image fusion, side window filtering, linear filter, infrared images, visible images.

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“…It is well known that MST-based methods [11]- [16] are the most researched fusion methods. Their main process includes three steps, i.e., decomposition, fusion, and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A General Perceptual Infrared and Visible Image Fusion Framework Based on Linear Filter and Side Window Filtering Technology

Yan

2020

IEEE Access

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“…The early fusion methods such as intensity-hue-saturation (IHS) and principal component analysis (PCA) were to process pixel values on spatial domain, which were traditional classical methods, but the fusion effect was limited compared with other excellent fusion methods [8][9][10]. Many fusion methods based on multiscale transform (MST) have become popular in recent years, such as Laplacian pyramid (LP), wavelet transform (WT), discrete wavelet transform (DWT), and nonsubsampled contourlet transform (NSCT) [11][12][13][14][15][16]. Due to the excellent characteristics of the multiscale decomposition method, the MST-based method could get a good fusion effect compared with early fusion methods, such as NSCT-PCNN [17].…”

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

Zhou

Zhang

et al. 2018

Journal of Sensors

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The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better preserve the interesting region and its corresponding detail information, a novel multiscale fusion scheme based on interesting region detection is proposed in this paper. Firstly, the MeanShift is used to detect the interesting region with the salient objects and the background region of IR and VI. Then the interesting regions are processed by the guided filter. Next, the nonsubsampled contourlet transform (NSCT) is used for background region decomposition of IR and VI to get a low-frequency and a series of high-frequency layers. An improved weighted average method based on per-pixel weighted average is used to fuse the low-frequency layer. The pulse-coupled neural network (PCNN) is used to fuse each high-frequency layer. Finally, the fused image is obtained by fusing the fused interesting region and the fused background region. Experimental results demonstrate that the proposed algorithm can integrate more background details as well as highlight the interesting region with the salient objects, which is superior to the conventional methods in objective quality evaluations and visual inspection.

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“…It can decompose images in arbitrary direction at any scale and is good at describing contours and directional texture of images [22]. Many researches focused on Contourlet transform combined with sparse representation [23,24], low-level visual features [25], object region detection [26], and other methods [27,28]. In order to solve the problem of unclear edge and textures, we proposed an improved Contourlet transform method combined with total variation (TV) model and local region energy.…”

Section: Introductionmentioning

confidence: 99%

Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

Guo

Zhou

Lin

et al. 2018

Journal of Advanced Transportation

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Objects intruding high-speed railway clearance do great threat to running trains. In order to improve accuracy of railway intrusion detection, an automatic multimodal registration and fusion algorithm for infrared and visible images with different field of views is presented. The ratio of the nearest to next nearest distance, geometric, similar triangle, and RANSAC constraints are used to refine the matching SURF feature points successively. Correct matching points are accumulated with multiframe to overcome the insufficient matching points in single image pair. After being registered, an improved Contourlet transform fusion algorithm combined with total variation and local region energy is proposed. Inverse Contourlet transform to low frequency subband coefficient fused with total variation model and high frequency subband coefficients fused with local region energy is used to reconstruct the fused image. The comparison to other 4 popular fusion methods shows that our algorithm has the best comprehensive performance for multimodal railway image fusion.

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Infrared and visible images fusion based on RPCA and NSCT

Cited by 95 publications

References 20 publications

A General Perceptual Infrared and Visible Image Fusion Framework Based on Linear Filter and Side Window Filtering Technology

A General Perceptual Infrared and Visible Image Fusion Framework Based on Linear Filter and Side Window Filtering Technology

Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform

Novel Registration and Fusion Algorithm for Multimodal Railway Images with Different Field of Views

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