Correlativity analysis between image gray value and temperature based on infrared target

Jiamin, 巩稼民 Gong; Tao, 郭 涛 Guo; Yi, 曹 懿 Cao; Huabo, 柳华勃 Liu; Beibei, 王贝贝 Wang

doi:10.3788/irla201645.0304006

Cited by 2 publications

(1 citation statement)

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“…The areas with higher temperatures indicate more severe heating conditions, which should be preserved and highlighted in the fused image. For infrared images, there is a direct proportional relationship between pixel values and temperature [ 33 ], and regions with high pixel values indicate areas with high temperatures.…”

Section: Proposed Methodsmentioning

confidence: 99%

TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images

Yan,

Zhao,

Miao

et al. 2024

Sensors

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The fusion of infrared and visible images is a well-researched task in computer vision. These fusion methods create fused images replacing the manual observation of single sensor image, often deployed on edge devices for real-time processing. However, there is an issue of information imbalance between infrared and visible images. Existing methods often fail to emphasize temperature and edge texture information, potentially leading to misinterpretations. Moreover, these methods are computationally complex, and challenging for edge device adaptation. This paper proposes a method that calculates the distribution proportion of infrared pixel values, allocating fusion weights to adaptively highlight key information. It introduces a weight allocation mechanism and MobileBlock with a multispectral information complementary module, innovations which strengthened the model’s fusion capabilities, made it more lightweight, and ensured information compensation. Training involves a temperature-color-perception loss function, enabling adaptive weight allocation based on image pair information. Experimental results show superiority over mainstream fusion methods, particularly in the electric power equipment scene and publicly available datasets.

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Section: Proposed Methodsmentioning

confidence: 99%