颜色恒定性
人工智能
保险丝(电气)
融合
计算机视觉
计算机科学
图像融合
人工神经网络
图像(数学)
模式识别(心理学)
物理
语言学
哲学
量子力学
作者
Xiaoling Zhou,Xiaoyue Jiang,Idowu Paul Okuwobi
标识
DOI:10.1016/j.image.2023.116956
摘要
To overcome detail loss problem of infrared and low-illumination visible light image fusion, this paper proposes a novel fusion framework based on Modified Pulse Coupled Neural Network (MPCNN) and Retinex theory. First, MPCNN is designed to segment original images into regions with different weights. Second, a novel Retinex-MPCNN algorithm is proposed to enhance low-illumination visible light image, details of which can be clearer. Then, a specific weighted fusion strategy based on region segmentations of MPCNN is designed to fuse the infrared and enhanced visible light images. Different from average fusion strategy, we introduce an illumination item to increase the attention for low-illumination areas, thereby preserves more details to the fusion image. Experimental results on TNO dataset demonstrate that our proposed method can generate fusion images with clear contour and structure information. Compared with existing fusion methods, our method achieves better performance both in subjective and objective assessment.
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