Multi-sensor image fusion based on fourth order partial differential equations

Bavirisetti, Durga Prasad; Xiao, Gang; Liu, Gang

doi:10.23919/icif.2017.8009719

Cited by 201 publications

(102 citation statements)

References 14 publications

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“…Multi-resolution Singular Value Decomposition (MSVD) [42] ，Fourth Order Partial Differential Equations (FPDE) [43] , Different Resolutions via Total Variation (DRTV) [44] and Visual Attention Saliency Guided Joint Sparse Representation (SGJSR) [45] .…”

Section: Comparison With the State-of-the-art Methodsmentioning

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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Section: Comparison With the State-of-the-art Methodsmentioning

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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“…These methods decompose visible-infrared images and reconstruct a new image according to the rules for fusion. Pyramid-based methods [1][2] [3], wavelet-based methods [4][5] [6], curvelet transform-based methods [7], multi-resolution singular value decomposition-based method [8], bilateral filter-based method [9], guided filtering-based methods [10][11] [12], sparse representation-based methods [13], fourth order partial differential based method [14], and visual saliency based method [15] [16] have been studied. Image decomposing and recomposing based fusion methods have shown superior results in terms of the quality of the fused image.…”

Section: Image Decomposing and Recomposing Based Image Fusionmentioning

confidence: 99%

Real-Time Visible-Infrared Image Fusion using Multi-Guided Filter

Jeong¹,

Han²,

Yang³

et al. 2019

KSII TIIS

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Visible-infrared image fusion is a process of synthesizing an infrared image and a visible image into a fused image. This process synthesizes the complementary advantages of both images. The infrared image is able to capture a target object in dark or foggy environments. However, the utility of the infrared image is hindered by the blurry appearance of objects. On the other hand, the visible image clearly shows an object under normal lighting conditions, but it is not ideal in dark or foggy environments. In this paper, we propose a multi-guided filter and a real-time image fusion method. The proposed multi-guided filter is a modification of the guided filter for multiple guidance images. Using this filter, we propose a real-time image fusion method. The speed of the proposed fusion method is much faster than that of conventional image fusion methods. In an experiment, we compare the proposed method and the conventional methods in terms of quantity, quality, fusing speed, and flickering artifacts. The proposed method synthesizes 57.93 frames per second for an image size of 320 × 270. Based on our experiments, we confirmed that the proposed method is able to perform real-time processing. In addition, the proposed method synthesizes flicker-free video.

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“…Furthermore, this is achieved with a lower complexity in comparison to algorithms that give similar results [6]. In [6] 18 different fusion methods [10,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] are compared using nine objective fusion performance metrics and computational complexity evaluations. The analysis concluded that out of the real-time capable fusion algorithms, the Laplacian fusion performs best for the majority of metrics.…”

Section: Dynamic Laplacian Rolling-pyramid Fusionmentioning

confidence: 99%

Rolling 3D Laplacian Pyramid Video Fusion

Pavlović

Petrović

2019

Electronics

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In this paper, we present a novel algorithm for video fusion of multi-sensor sequences applicable to real-time night vision systems. We employ the Laplacian pyramid fusion of a block of successive frames to add temporal robustness to the fused result. For the fusion rule, we first group high and low frequency levels of the decomposed frames in the block from both input sensor sequences. Then, we define local space-time energy measure to guide the selection based fusion process in a manner that achieves spatio-temporal stability. We demonstrate our approach on several well-known multi-sensor video fusion examples with varying contents and target appearance and show its advantage over conventional video fusion approaches. Computational complexity of the proposed methods is kept low by the use of simple linear filtering that can be easily parallelised for implementation on general-purpose graphics processing units (GPUs).

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Multi-sensor image fusion based on fourth order partial differential equations

Cited by 201 publications

References 14 publications

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

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

Real-Time Visible-Infrared Image Fusion using Multi-Guided Filter

Rolling 3D Laplacian Pyramid Video Fusion

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