Poisson Reconstruction-Based Fusion of Infrared and Visible Images via Saliency Detection

Li, Jing; Huo, Hongtao; Sui, Chenhong; Jiang, Chenchen; Li, Chang

doi:10.1109/access.2019.2897320

Cited by 17 publications

(6 citation statements)

References 64 publications

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“…If the cruel wolf gets a whiff of prey, Y i > Y L , set Y L = Y i and the cruel wolf will replace alpha wolf to perform the summoning behavior. If Y i > Y L , the cruel wolf will continue attacking until d is ≤ d near ; (3.4) Update the positions of the intelligent wolves that participate in the beleaguering behavior according to formula (22), and then execute the beleaguering behavior;…”

Section: Fusion Schemementioning

confidence: 99%

“…The main related methods include the Wavelet Transform (WT) [5], Curvelet Transform (CT) [6], Non-Sampled Contourlet Transform (NSCT) [7], Non-Sampled Shearlet Transform (NSST) [8], [9], Directionlet Transform [10], empirical mode decomposition [11], internal generative mechanism [12], multiresolution singular value decomposition [13], Tetrolet Transform (TT) [14], Top-hat transform [15], Sparse Representation (SR) [16] and Total Variation (TV) decomposition method [17]- [19]. The spatial domain method directly extracts useful information from fusion in the spatial domain without a decomposition or reconstruction step and includes the significance fusion method and subspace-based fusion method [8], [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

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Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Feng

Lou

2020

IEEE Access

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The purpose of image fusion is to merge a substantial amount of information, such as contour, texture and intensity distribution information from original images, with a fusion image. To retain a considerable amount of the fusion image while retaining the texture details of the source image and maintaining the edge of the source image, this paper proposes an improved infrared and visible image fusion algorithm that is based on total variation. First, source infrared and visible light images and a difference image were decomposed by a total variation model, and their respective cartoon and texture components were acquired. A fitness function was solved according to the entropy, standard deviation and edge similarity of the infrared and visible light images. The optimal combination weight of various kinds of texture and cartoon components was sought via a wolf pack intelligence optimization algorithm to acquire the final fusion results with high-quality contrast ratio and edge details. The experimental results indicate that the proposed method not only can preserve edge contour information about the original image but also can effectively retain its texture detail information. The method is superior to the traditional multiscale and sparse representation fusion method with regard to various indicators, such as subjective visual effect, mutual information, gradient information, structural similarity and visual sensitivity. INDEX TERMS Infrared and visible light, image fusion, total variation model, wolf pack algorithm.

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Section: Fusion Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Feng

Lou

2020

IEEE Access

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“…Wherein, the contour information is crucial for characterizing a defect, so the fusion algorithm needs to be able to efficiently preserve not only as much information as possible for each IR reconstruction, but also the edge contour information of the defects in each reconstruction. Then the image fusion algorithm based on guide filtering [31] is used to carry out infrared reconstruction image fusion. In the end, the above method realized the homologous heterogeneous coupling defect IR reconstruction image fusion, heterologous heterogeneous plural quantity IR reconstruction image fusion, and heterologous and homogeneous multi IR reconstruction image fusion.…”

Section: Introductionmentioning

confidence: 99%

Infrared Detection Method for Hypervelocity Impact Based on Thermal Image Fusion

et al. 2021

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As the number of space debris has increased rapidly in recent years, it poses a major threat to the safety of spacecraft in space, and damage assessment for space debris hypervelocity impact is very important. In order to more comprehensively and accurately describe the damage defects in the infrared inspection data collected by infrared thermal imaging technology, this paper proposes an ultra-high-speed impact damage detection algorithm based on infrared reconstruction image fusion. The algorithm first preprocesses the obtained infrared thermal response image sequence and separates the damage features, and then applies a multi-objective evolutionary optimization algorithm to extract the typical transient thermal response, and then reconstructs the feature infrared images. Finally, image fusion based on guided filtering is performed on the damage reconstruction infrared images. In this paper, several reconstruction images that represent different types of impact damage defect are merged together to improve the detection ability of the algorithm. Infrared detection experiments on damaged specimens obtained from actual hypervelocity impacts verify the effectiveness of the proposed algorithm.

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“…Many different approaches have been proposed for infrared object tracking such as saliency extraction [9], multiscale patch-based contrast measure and a temporal variance filter [14], feature learning and fusion, reliability weight estimation based on nonnegative matrix factorization [15], Poisson reconstruction and the Dempster-Shafer theory [16], three-dimensional scalar field [17], a double-layer region proposal network (RPN) [18], Siamese convolution network [19], a mixture of Gaussians with modified flux density [20], spatial-temporal total variation regularization and weighted tensor [21], two-stage U-skip context aggregation network [22], histogram similarity map based on the Epanechnikov kernel function [23], quaternion discrete cosine transform [24], non-convex optimization [25], Mexican-hat distribution of pixels [26], and Schatten regularization with reweighted sparse enhancement [27].…”

Section: Introductionmentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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Poisson Reconstruction-Based Fusion of Infrared and Visible Images via Saliency Detection

Cited by 17 publications

References 64 publications

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Infrared Detection Method for Hypervelocity Impact Based on Thermal Image Fusion

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

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