Target-Aware Fusion of Infrared and Visible Images

Zhou, Yingjie; Gao, Kun; Dou, Zheng; Hua, Zizheng; Wang, Hong

doi:10.1109/access.2018.2870393

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…Fig.2 depicts a framework of the algorithm we proposed herein and related algorithms. The following algorithms are similar to ours: GTF [20], ALF [30], TAD-PGF [29]. Inspired by these articles, we proposed the algorithm of this paper.…”

Section: The Proposed Fusion Methodsmentioning

confidence: 90%

“…The blue box in the figure represents the algorithm proposed in this paper. The red box is the GTF algorithm proposed by Ma et al [20], and the green box is the ALF algorithm proposed by Guo et al [30], and the purple box is the TAD-PGF algorithm proposed by Zhou et al [29].…”

Section: The Proposed Fusion Methodsmentioning

confidence: 99%

“…Next, we add the gradient direction to obtain more accurate constraint information. Inspired by [29], we use the inner product to further control the direction of the gradient to obtain a more accurate fusion result, which is expressed as follows: (17) where ∇u d , ∇f 2d represents the inner product of the fused image and detail layer of the visible image, and it is used to constraints the gradient of the fused image. The inner product here can be expanded to: − θ ∇f 2d ) is the difference in the gradient direction of the two.…”

Section: ) Design Of Texture Information Retention Itemmentioning

confidence: 99%

“…As a result, we end up expressing the fusion problem as a minimization problem in order to obtain a fused image with significant targets and details. This model was inspired by the fusion models of Ma et al [20] and Zhou et al [29], and their models both achieved excellent results. The method of Ma et al is based on the total variation denoising model, which directly constrains brightness and gradient of the image.…”

Section: ) Design Of Texture Information Retention Itemmentioning

confidence: 99%

“…However, the author ignores part of the information of the visible image, which may result in loss of details in the image. Zhou et al [29] proposed a method called targetaware fusion, which introduced the concept of inner product to strengthen the control of gradient modulus and gradient direction. The fusion result is good and the visual information is richer, however, multiple controls are not absolutely necessary and have an adverse effect on computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Infrared and Visible Image Fusion Based on Gradient Transfer Optimization Model

2020

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To tackle the problem of partial loss of image details in infrared and visible image fusion, a gradient transfer optimization model is proposed for the fusion of infrared and visible images. Firstly, an adaptive image decomposition method is proposed based on coupled partial differential equation, the infrared image and the visible image are decomposed into base layer and detail layer to extract the highbrightness target and the details of the two images. Based on this superior information of infrared image and visible image, the optimization model is designed to obtain the fusion image obvious target and rich details. For the proposed optimization model, Alternating Direction Method of Multipliers (ADMM) is used to decompose the original model into sub-problems that are easy to solve and iteratively optimize to obtain the optimal solution. The introduction of control parameters makes the model more flexible in different situations, and retains the thermal radiation information of the infrared image and the detailed information of the visible image to the greatest extent. The fused image visual effects and performance indicators are improved. We completed the experiment using a public data set and analyzed the experimental results. The experimental results show that the proposed method can better preserve the clear target and texture information of infrared and visible images, and the fusion results are more accurate and comprehensive. The experiment results also indicate that our method performs well and achieves comparable metric values with the state-of-the-art methods. INDEX TERMS Image fusion, optimization model, ADMM, partial differential equation.

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Section: The Proposed Fusion Methodsmentioning

confidence: 90%