An Image Fusion Method Based on Sparse Representation and Sum Modified-Laplacian in NSCT Domain

Li, Yuanyuan; Sun, Yanjing; Huang, Xinhua; Qi, Guanqiu; Zheng, Mingyao; Zhu, Zhiqin

doi:10.3390/e20070522

Cited by 67 publications

(41 citation statements)

References 47 publications

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“…The ROC curve describes the relationship between the probability of detection (Pd) and the false alarm rate (Fa). The definitions of Pd and Fa are as follows: Pd = number of detected true targets total number of real targets (14) Fa = number of detected false targets total number of pixels in the whole image (15) Figure 7 shows the ROC curves of six methods for eight real images. Our method has better performance than baseline methods and possesses higher Pd but lower Fa, compared with the baseline methods, especially for Sequence 6, 7, and 8.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The Contourlet transform can make the most of the geometric characteristics of data, such as line singularities and plane singularities. Since contourlet transform contains the downsampling, it lacks shift invariant property [14]. Researchers have proposed Nonsubsampled contourlet transform (NSCT) to describe complex spatial structures in various directions well.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Xin

2020

Sensors

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Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Xin

2020

Sensors

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“…NSCT can overcome the frequency aliasing phenomenon caused by upsampling and downsampling on CT [18,19]. NSCT is a discrete image calculation framework that achieves shift-invariant, multi-scale, and multi-direction by using non-subsampled pyramid filter banks (NSPFBs) and non-subsampled directional filter banks (NSDFBs).…”

Section: Nsctmentioning

confidence: 99%

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

Huang

Wei

et al. 2019

Entropy

Self Cite

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In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high-and low-frequency components, the corresponding decomposed components are not precise enough for the following infrared-visible fusion operations. This paper proposes a non-subsampled contourlet transform (NSCT) based decomposition method for image fusion, by which source images are decomposed to obtain corresponding high-and low-frequency sub-bands. Unlike MST, the obtained high-frequency sub-bands have different decomposition layers, and each layer contains different information. In order to obtain a more informative fused high-frequency component, maximum absolute value and pulse coupled neural network (PCNN) fusion rules are applied to different sub-bands of high-frequency components. Activity measures, such as phase congruency (PC), local measure of sharpness change (LSCM), and local signal strength (LSS), are designed to enhance the detailed features of fused low-frequency components. The fused high-and low-frequency components are integrated to form a fused image. The experiment results show that the fused images obtained by the proposed method achieve good performance in clarity, contrast, and image information entropy.Entropy 2019, 21, 1135 2 of 16 for infrared-visible image fusion, have been widely applied to the target recognition in different environments, such as smart city, battlefield, remote sensing, and so on [2,3].In recent years, transform domain based methods have become the mainstream in infrared-visible image fusion, which include pyramid, wavelet transform, multi-scale geometric transform, sparse representation [4,5], and so on. Pyramid, wavelet transform, and multi-scale geometric transform can be categorized as MST-based methods. MST-based methods have three main steps. First, MST is employed to decompose each source image into high-frequency sub-bands at different scales and directions as well as one low-frequency sub-band. Then, the obtained high-and low-frequency sub-bands are fused separately following different fusion rules. Finally, the fused image is obtained by performing the inverse MST on both fused high-and low-frequency sub-bands. Double-tree complex wavelet transform as a kind of wavelet transform can only capture a limited amount of edge information, but cannot correctly and effectively represent the discontinuity of lines and curves [6]. As a true two-dimensional (2D) multi-scale geometric analysis method, contourlet transform (CT) possesses localization, multi-resolution, multi-scale, multi-direction, and anisotropy.As a shift-invariant version of CT, NSCT performs well in transform domain, and has been widely used in image fusion. NSCT has multi-scale and multi-direction features, which can solve the limitations of traditional wavelet methods in the representation of image curves and edges [7]. Compared with traditional MST-based image fusion methods, NSCT has shif...

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“…The key to MGA-based methods is the MGA transform, which decides the amount of the useful information that can be extracted from source images and integrated in the fused image. Popular transforms used for decomposition and reconstruction include Wavelet Transform [23] (WT), Wedgelet Transform [24] , Curvelet Transform [25] [26] , Contourlet Transform [27] , NSCT [28] [29] , Shearlet Transform [30] (ST), Non-Subsampled Shearlet Transform [31] (NSST) and so on. Due to the characteristics of shift-invariant, high sensitivity, strong directivity, fast operation speed, and multi-directional processing, NSST has been widely used in the image fusion [32] .…”

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

Xing

LIU

Luo

et al. 2020

Preprint

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In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the Wavelet Transform (WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

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An Image Fusion Method Based on Sparse Representation and Sum Modified-Laplacian in NSCT Domain

Cited by 67 publications

References 47 publications

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

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